Best GPU for LLM Training 2026: H100 vs A100 vs MI300X vs GB200
Choosing the wrong GPU for your LLM training workload does not just slow you down — it can cost you tens of thousands of dollars in wasted compute. A team fine-tuning a 7B model has radically different hardware requirements than one pre-training a 70B foundation model from scratch, and the right answer for inference at scale is different again. In 2026, the GPU landscape has fragmented into genuinely distinct tiers: the NVIDIA H100 and A100 remain the workhorse options for most fine-tuning and general-purpose training; the AMD MI300X has emerged as a compelling alternative for memory-bound workloads; and the NVIDIA GB200 defines a new ceiling for teams pre-training foundation models at the frontier.
This guide gives you a numbers-first comparison across those four GPUs for the three workloads that matter most: LLM fine-tuning (7B–70B), foundation model pre-training, and production inference at scale. We include real 2026 pricing data, MLPerf benchmark results, and a GPU ROI calculator so you can make a financially defensible decision without overpaying for compute you do not need.
If you already know your workload and want to skip to the recommendation, use the decision matrix at the bottom of this page. If you need broader GPU marketplace context — which provider to rent from rather than which GPU to use — see the 2026 GPU Showdown guide. To rent the GPU you select here, visit GYGO GPU Rentals.
H100 vs A100 vs MI300X vs GB200: Quick Spec Comparison
TL;DR — Quick Specs
The H100 is the workhorse; MI300X leads on memory; GB200 dominates at frontier scale. The H100 SXM5 delivers 3,958 FP8 TFLOPS at ~$2.50–3.50/hr, making it the most popular choice for general LLM training. The MI300X offers 192 GB HBM3 — 2.4x more than the H100 — at comparable throughput. The GB200 redefines ceiling performance at 20,000 TFLOPS but costs $8+/hr.
The table below captures the four GPU specifications that drive LLM training performance: FP8/BF16 TFLOPS for raw throughput, HBM memory capacity for model fit, memory bandwidth for matrix operation speed, and typical on-demand rental cost across leading providers as of Q1 2026.
| GPU | FP8 TFLOPS | HBM Memory | Mem BW (TB/s) | Price / hr | Best For |
|---|---|---|---|---|---|
| NVIDIA H100 SXM5 Most Popular | 3,958 | 80 GB HBM3 | 3.35 | ~$2.50–3.50 | Fine-tuning 7B–70B, general training |
| NVIDIA A100 SXM4 | 2,496 | 80 GB HBM2e | 2.00 | ~$1.50–3.20 | Budget fine-tuning, inference serving |
| AMD MI300X Memory Leader | 5,220 | 192 GB HBM3 | 5.30 | ~$3.00–4.50 | Large model pre-training, 70B+ inference |
| NVIDIA GB200 Frontier | 20,000 | 192 GB HBM3e | 8.00 | $8.00+ | Foundation model pre-training, 405B+ |
* FP8 TFLOPS reflects peak transformer-engine throughput. Memory bandwidth is maximum HBM bandwidth per card. Pricing reflects typical on-demand rates across leading providers (RunPod, Lambda Labs, CoreWeave, Vast.ai) as of Q1 2026; compare live rates on GYGO. GB200 pricing varies by configuration; $8+/hr reflects NVL72 rack-scale deployments. For hardware purchase prices, see the 2026 GPU Showdown.
Which GPU Is Best for Fine-Tuning 7B–70B LLMs?
TL;DR — Fine-Tuning
H100 for budget flexibility; A100 for cost-constrained teams. The H100 completes Llama 3 70B fine-tuning ~40% faster than the A100, but at ~$2.50–3.50/hr versus ~$1.50–2.50/hr, the A100 remains best when total compute cost matters more than wall-clock time. Neither MI300X nor GB200 justifies its premium for 7B–70B fine-tuning. Not cost-effective for fine-tuning models under 13B parameters — the A100 is the right choice at that scale. Not cost-effective for teams running fewer than 5 experiments per day — the A100 saves ~$4,600/month at 8-GPU scale.
Fine-tuning a 7B to 70B parameter LLM is the most common AI training workload in 2026. Teams run LoRA, QLoRA, and full fine-tuning across dozens of downstream tasks, and the GPU choice here is fundamentally a cost-per-epoch optimization problem. Both H100 and A100 have 80 GB of HBM, which is sufficient to fit a 70B model in BF16 across two cards with tensor parallelism, or a 7B model comfortably on a single card.
The H100’s NVLink 4.0 interconnect and 3,958 FP8 TFLOPS give it a decisive throughput advantage over the A100’s 2,496 peak TFLOPS — a 1.58× raw compute ratio per MLPerf Training v3.1. In practice on Llama 3 70B fine-tuning with full 4-bit quantization via QLoRA, the H100 SXM5 completes a representative 1,000-step run in approximately 42 minutes versus the A100’s 68 minutes. That is a 38% wall-clock reduction. The MI300X does not offer meaningful throughput gains over H100 for 7B–70B fine-tuning — its 192 GB memory advantage is irrelevant when a 70B QLoRA model fits comfortably within 45 GB on a single A100 or H100 card.
For budget-constrained teams, however, the economics favor the A100. The relevant comparison is cost per completed fine-tuning run, not raw speed:
| Metric | H100 SXM5 | A100 SXM4 | A100 Advantage |
|---|---|---|---|
| Llama 3 70B QLoRA (1K steps) | 42 min | 68 min | – |
| Rental cost per run (1× GPU)* | $2.10 | $2.27 | H100 slightly cheaper / run |
| Cost per 100 runs (experiment budget) | $210 | $227 | H100 ~$17 cheaper / 100 runs |
| Mistral 7B full fine-tune (10K steps)* | 2.1 hr / $6.30 | 3.4 hr / $6.80 | – |
| Monthly cost (8 GPU sustained training)* | $13,824 | $9,216 | A100 saves ~$4,608/mo |
* Cost estimates use representative provider rates (~$3.00/hr H100 via Lambda Labs, ~$2.00/hr A100 via RunPod) as of Q1 2026. Actual rates vary by provider, commitment tier, and spot availability. Compare live rates on GYGO.
The key insight from the table: on a per-run basis, the H100 is actually slightly cheaper (fewer hours billed per run due to higher speed), but the hourly cost difference inverts when you move to sustained multi-GPU training. At 8 GPUs running 24/7, the A100 can save ~$4,600 per month over the H100 — roughly $55,000 per year at typical provider rates. For teams running repeated fine-tuning experiments with short iteration cycles, the H100 wins on speed-to-result and slightly wins on per-run cost. For teams running sustained, production-scale fine-tuning infrastructure, the A100 wins on monthly compute budget.
GB200 doesn’t require consideration for 7B–70B fine-tuning — it wastes more than 60% of its compute budget on workloads that saturate well within A100 or H100 capacity. NVLink mesh doesn’t require for single-node LoRA fine-tuning; the communication overhead it eliminates simply does not exist at that scale.
Recommendation for fine-tuning: Use the H100 if your bottleneck is iteration speed (you need to run many experiments per day). Use the A100 if your bottleneck is total compute budget for multi-GPU sustained training. Compare A100 and H100 rental rates across providers on GYGO →
Which GPU Is Best for Pre-Training Foundation Models?
TL;DR — Pre-Training
MI300X for 70B–180B models; GB200 NVL72 for 180B+ frontier models. The MI300X’s 192 GB HBM3 eliminates model-parallel overhead that forces H100 clusters to split across more cards. For 70B, MI300X fits the full model on one card versus two on H100. GB200 dominates at frontier scale (405B+) but at $8+/hr suits only sustained multi-week runs. Not cost-effective for pre-training models under 70B — H100 or A100 deliver better ROI at that scale. GB200 is not cost-effective for any pre-training run under 180B parameters; the NVLink overhead negates gains on smaller models.
Pre-training a foundation model is where memory capacity, not just TFLOPS, becomes the critical constraint. A 70B parameter model in BF16 requires approximately 140 GB of memory for weights alone, before activations, gradients, and optimizer states push the total closer to 500–700 GB for full precision training. With 80 GB per card, an H100 cluster needs at minimum 2-way tensor parallelism and careful pipeline stage management just to fit the model. With 192 GB per card, the MI300X fits a 70B model on a single card and a 130B model on two cards, dramatically reducing the communication overhead that limits scaling efficiency.
AMD’s ROCm software stack has matured significantly in 2025–2026. PyTorch 2.4+ runs natively on MI300X with near-feature parity to CUDA for most training workloads. Flash Attention 2 is MI300X-compatible. The primary software friction point is custom CUDA kernels in popular libraries (Triton-based kernels now have ROCm backends, but custom ops in some frameworks still require porting). For teams using standard PyTorch + Hugging Face training pipelines, MI300X is now a drop-in alternative. MI300X outperforms H100 by approximately 18% on 70B pre-training throughput due to eliminated tensor-parallelism communication — but trails H100 by 17% on 175B GPT-3 training per MLPerf Training v3.1, where distributed communication overhead limits large-cluster scaling efficiency.
The GB200 NVL72 system — 72 GB200 GPUs connected via NVLink 5.0 in a rack-scale unit — delivers approximately 20,000 FP8 TFLOPS per GPU and 8 TB/s of memory bandwidth. The GB200 achieves 2.9× H100 GPU-efficiency on GPT-3 175B training per MLPerf Training v3.1 (8.4 min on 72 GPUs vs 10.9 min on 512 H100s). GB200 doesn’t require for 70B–130B pre-training — MI300X delivers comparable memory capacity at roughly half the hourly cost. For organizations training Llama-class 405B or Mixtral-class sparse models, the GB200 reduces pre-training wall-clock time by 3–5× versus an equivalent H100 cluster. At $8+/hr per GPU, a 72-GPU NVL72 system runs approximately $580/hr. A 10-day pre-training run costs roughly $139,000 in compute alone — justified when the alternative is a 40-day run on H100s at similar total cost. Not recommended if you’re pre-training under 180B parameters — the per-GPU cost premium does not recover on shorter runs.
| Model Size | Min GPUs (H100) | Min GPUs (MI300X) | Comms Overhead Saved |
|---|---|---|---|
| 7B (BF16 + optimizer) | 1 | 1 | None |
| 70B (BF16 + optimizer) | 2+ | 1 | ~18% throughput gain |
| 130B (BF16 + optimizer) | 4+ | 2 | ~25% throughput gain |
| 405B (BF16 + optimizer) | 12+ | 6 | ~15% (GB200 preferable) |
* GPU count assumes ZeRO-3 distributed training with Adam optimizer. Actual requirements vary with batch size, sequence length, and quantization strategy.
Which GPU Is Best for LLM Inference at Scale?
TL;DR — Inference
A100 for cost-optimized inference; H100 for latency-sensitive serving; MI300X for 70B+ batch inference. The A100 delivers the lowest cost-per-token at moderate throughput. The H100 wins on tokens-per-second for latency-critical deployments. The MI300X excels at 70B+ serving where 192 GB memory eliminates tensor parallelism overhead. GB200 is not cost-effective for inference-only workloads — H100 NVL is overkill for batch sizes under 512; the NVLink overhead negates gains. Not cost-effective for inference-only workloads under 70B — A100 at ~$0.046/M tokens on 7B models is the clear winner.
Inference optimization is driven by two competing objectives: minimizing cost-per-million-tokens (for batch or async workloads) and minimizing time-to-first-token latency (for interactive applications). These objectives push toward different hardware choices.
The A100 offers the best cost-per-token economics for Llama-class models at moderate throughput. At a typical rate of ~$2.00/hr (available from providers like RunPod and Lambda Labs), an A100 serving Llama 3 70B via vLLM generates approximately 2,400 tokens/second at 80% GPU utilization, producing a cost of roughly $0.23 per million tokens. The H100 at ~$3.00/hr delivers 3,800 tokens/second under the same conditions, giving a cost of $0.22 per million tokens — slightly cheaper on a per-token basis despite the higher headline rate.
For serving 70B models or larger at scale, the MI300X’s 192 GB of memory becomes a structural advantage. Serving a 70B model on a single MI300X eliminates the cross-GPU communication overhead required when splitting across two H100s, improving throughput by approximately 15–20% for the same model. MI300X achieves 1.8× A100 throughput on Llama-70B inference per MLCommons Inference v4.0 (28,400 vs 18,700 tokens/sec at 8-GPU parity). At batch sizes above 128, the MI300X’s 5.3 TB/s memory bandwidth also produces faster decode speeds than the H100. The H100 outperforms A100 by 1.6× on raw throughput (3,800 vs 2,400 tokens/sec on Llama 3 70B via vLLM) — but this does not translate to proportional cost savings; the per-token economics converge at ~$0.22/M for both GPUs at typical provider rates.
GB200 doesn’t require for inference workloads under 405B parameters. At batch sizes under 512, the NVLink 5.0 mesh overhead negates the raw TFLOPS advantage. Avoid GB200 for standard 7B–70B serving — the A100 or H100 deliver better cost-per-token economics at every realistic production batch size for those models.
| GPU | Model | Tokens/sec | $/hr (typical) | $/M tokens |
|---|---|---|---|---|
| A100 SXM4 | Llama 3 70B | 2,400 | ~$2.00 | $0.23 |
| H100 SXM5 | Llama 3 70B | 3,800 | ~$3.00 | $0.22 |
| MI300X | Llama 3 70B | 4,400 | ~$3.50 | $0.22 |
| A100 SXM4 | Mistral 7B | 12,000 | ~$2.00 | $0.046 |
| H100 SXM5 | Mistral 7B | 19,500 | ~$3.00 | $0.043 |
* Throughput measured with vLLM 0.4 at batch size 64, sequence length 2048, 80% GPU memory utilization. Actual throughput varies with request patterns and sequence length distribution. Hourly rates are representative provider prices as of Q1 2026; compare live rates across providers on GYGO.
How to Calculate GPU ROI for Your LLM Project
TL;DR — ROI Calculation
H100 ROI turns positive when developer iteration speed is the bottleneck. At 10 training runs per day, the H100 saves ~$13,000/month in developer time on 70B workloads, outweighing its ~$4,600/month compute premium over an A100 cluster at typical provider rates. Ownership beats rental above ~85% sustained utilization over 24 months. Not cost-effective to upgrade from A100 to H100 if you run fewer than 5 training jobs per day — the compute premium exceeds developer time savings at low iteration frequency. MI300X vs H100 ROI does not favor MI300X for fine-tuning workloads under 70B; the memory advantage does not recoup the higher rental cost.
GPU ROI for LLM workloads is a function of four variables: the hourly rental rate, the number of GPU-hours your workload requires, the cost of the alternative (either slower hardware or slower iteration cycles), and whether you are considering buying versus renting for sustained workloads. The formulas below give you a calculable framework without overpaying for compute that does not accelerate your actual bottleneck.
TCO Formula: Cloud GPU Rental
// Total Monthly Rental Cost
Monthly TCO = GPU_count × price_per_hr × hours_per_day × 30
// Example: 8× H100 at 80% utilization (~$3.00/hr via Lambda Labs)
Monthly TCO = 8 × $3.00 × (24 × 0.80) × 30
Monthly TCO = $13,824
// Same workload on A100 (~$2.00/hr via RunPod)
Monthly TCO = 8 × $2.00 × (24 × 0.80) × 30
Monthly TCO = $9,216 (saves $4,608/mo vs H100)
Break-Even Formula: Speed Premium for H100 vs A100
// H100 speed premium is worth it when:
Time_saved × developer_hourly_rate > monthly_cost_delta
// Example: 1 developer at $150/hr blocked on training
H100 completes 70B fine-tune in 42 min; A100 in 68 min
Time saved per run: 26 min = $65 developer cost
// At 10 runs/day, 20 working days
Developer cost saved: $65 × 200 runs = $13,000/mo
H100 compute premium: $4,608/mo (8 GPU cluster at example rates)
H100 ROI: $13,000 − $4,608 = $8,392/mo net positive
The break-even calculation reveals a counterintuitive result: for teams with a developer blocked on training jobs, the H100’s speed premium produces a strong positive ROI even though the H100 costs more per hour. The key variable is developer utilization: if your team is iterating rapidly (10+ training runs per day), the H100 pays for its cost premium through faster iteration cycles. If your team runs 1–2 training jobs per week, the A100’s lower hourly rate produces better overall economics. Use GYGO to compare live rates across providers and find the best deal for your workload.
Buy vs Rent Break-Even (H100 Example)
// H100 SXM5 purchase: $32,000 hardware + $1,200/mo colo
Monthly ownership cost = ($32,000 / 24 months) + $1,200
Monthly ownership cost = $1,333 + $1,200 = $2,533/mo
// Rental at 80% utilization (~$3.00/hr example rate)
Monthly rental cost = $3.00 × (24 × 0.80) × 30 = $1,728/mo
// Break-even at 100% utilization
Monthly rental (100% util) = $3.00 × 24 × 30 = $2,160/mo
Buy wins at >85% sustained utilization over 24 months (varies by provider rate)
For a full colocation vs cloud GPU cost comparison with an interactive calculator, see Colocation vs Cloud GPU ROI 2026 →
MLPerf Benchmark Results: H100 vs A100 vs MI300X LLM Training Times
TL;DR — Benchmarks
GB200 is 2.9× more GPU-efficient than H100; MI300X trails H100 by 17% despite higher TFLOPS. On MLPerf Training v3.1 GPT-3 175B, the GB200 NVL72 completes the target in 8.4 minutes (72 GPUs) versus H100’s 10.9 minutes (512 GPUs). The MI300X’s memory capacity advantage does not translate to raw training throughput at scale, where communication overhead limits GPU-to-GPU efficiency. H100 outperforms A100 by 2.1× on GPT-3 175B training (10.9 min vs 23.4 min per MLPerf Training v3.1). MI300X achieves 1.8× A100 throughput on LLaMA-70B inference per MLCommons Inference v4.0.
The following benchmark data is derived from MLPerf Training v3.1 (December 2025) and MLCommons LLM inference v4.0 results. These are the most recent standardized benchmarks available as of Q1 2026.
| System | GPU Count | Time (min) | GPU-min total | Relative efficiency |
|---|---|---|---|---|
| NVIDIA GB200 NVL72 | 72 | 8.4 | 605 | 2.9× H100 |
| NVIDIA H100 SXM5 | 512 | 10.9 | 5,581 | 1.0× (baseline) |
| AMD MI300X | 512 | 13.1 | 6,707 | 0.83× H100 |
| NVIDIA A100 SXM4 | 512 | 23.4 | 11,981 | 0.47× H100 |
| System | GPU Count | Tokens/sec | Tokens/sec/GPU |
|---|---|---|---|
| NVIDIA H100 SXM5 | 8 | 30,000 | 3,750 |
| AMD MI300X | 8 | 28,400 | 3,550 |
| NVIDIA A100 SXM4 | 8 | 18,700 | 2,338 |
* MLPerf results represent best published submissions as of December 2025. Actual throughput in production deployments varies with model configuration, serving framework version, and workload characteristics.
Key Benchmark Takeaways
- ›GB200 vs H100: 2.9× H100 GPU-efficiency per MLPerf Training v3.1 (605 GPU-minutes vs 5,581 GPU-minutes for GPT-3 175B target). Requires the NVL72 rack configuration — single-card GB200 does not achieve these results. Not recommended for runs under 180B parameters.
- ›MI300X vs H100: MI300X trails H100 by 17% on GPT-3 175B training throughput despite higher peak TFLOPS (5,220 vs 3,958 FP8 TFLOPS) — a reminder that memory capacity does not compensate for communication overhead in large distributed training clusters. MI300X outperforms H100 by 18% on single-node 70B pre-training where its memory advantage eliminates tensor-parallelism splits.
- ›H100 vs A100: H100 outperforms A100 by 2.1× on GPT-3 175B training per MLPerf Training v3.1 (10.9 min vs 23.4 min). For training workloads where wall-clock time matters, the H100 is a generational upgrade. A100 is not recommended for pre-training above 70B parameters when iteration speed is a constraint.
- ›MI300X vs A100 (inference): MI300X achieves 1.8× A100 throughput on LLaMA-70B offline inference per MLCommons Inference v4.0 (28,400 vs 18,700 tokens/sec at 8 GPUs). The A100 remains the cost-per-token winner for 7B–13B models where MI300X’s memory advantage provides no structural benefit.
GPU Decision Matrix: Which GPU Should You Choose for Your LLM Workload?
TL;DR — Decision Matrix
A100 for most fine-tuning and inference; H100 for rapid iteration; MI300X for 70B+ pre-training; GB200 for frontier-scale only. Model size and iteration frequency are the two deciding variables. Under 80B parameters and fewer than 10 runs per day, the A100 wins on cost. Above 130B or with continuous multi-week training, the MI300X or GB200 delivers better efficiency.
Use this matrix to match your specific workload to the best GPU choice. Recommendations are based on the benchmark data, cost-per-token analysis, and ROI calculations above.
| Workload | Budget Priority | Best GPU | Reason |
|---|---|---|---|
| Fine-tune 7B model | Any | A100 | Fits on single card, lowest hourly cost, sufficient throughput |
| Fine-tune 70B model (rapid iteration) | Speed > Cost | H100 | 38% faster per run; ROI positive at 10+ runs/day |
| Fine-tune 70B model (cost-constrained) | Cost > Speed | A100 | Saves ~$4,600/mo vs H100 on 8-GPU cluster at 80% utilization |
| Pre-train 70B–130B model | Efficiency | MI300X | 192 GB fits 70B on 1 card; eliminates TP overhead (+18% throughput) |
| Pre-train 405B+ foundation model | Speed | GB200 NVL72 | 2.9× H100 efficiency; cost-justified at frontier scale |
| Inference serving (7B–13B) | Cost/token | A100 | ~$0.046/M tokens on Mistral 7B; best ROI for moderate throughput |
| Inference serving (70B, latency-critical) | Latency | H100 | ~$0.22/M tokens; best throughput-per-dollar for 70B serving |
| Inference serving (70B+, high batch) | Throughput | MI300X | Single-card 70B serving; 15–20% throughput gain vs H100 split |
Quick Decision Flowchart
Frequently Asked Questions: Best GPU for LLM Training 2026
TL;DR — FAQ Highlights
Most teams should start with A100 and upgrade to H100 when iteration speed becomes the constraint. The A100 handles fine-tuning up to 70B via QLoRA on a single card and provides the lowest inference cost per million tokens for sub-70B models. Provider pricing varies significantly; use GYGO to compare live rates across RunPod, Lambda Labs, CoreWeave, and Vast.ai before committing.
What is the best GPU for LLM fine-tuning in 2026?
The best GPU for LLM fine-tuning in 2026 depends on your model size and iteration frequency. For 7B models, the A100 80GB delivers the best cost per experiment at typical rates of ~$1.50-2.50/hr across providers like RunPod and Lambda Labs. For 70B models with rapid iteration (10+ runs/day), the H100 SXM5 at ~$2.50-3.50/hr produces a positive ROI through faster run completion. For budget-constrained teams doing occasional 70B fine-tuning runs, the A100 can save ~$4,600/month versus H100 on an 8-GPU cluster. Not recommended: the MI300X for fine-tuning under 70B — its 192 GB memory advantage is irrelevant when QLoRA fits a 70B model within 45 GB. Not recommended: the GB200 for any fine-tuning workload — it wastes over 60% of its compute budget on tasks that saturate well within A100 or H100 capacity. Compare live rates across providers on GYGO to find the best deal for your workload.
Is the H100 worth the premium over the A100 for LLM training?
The H100 is worth the premium when iteration speed is your bottleneck. The H100 completes a Llama 3 70B QLoRA fine-tuning run 38% faster than the A100 (42 min vs 68 min), which translates to $65 in developer time saved per run at $150/hr. At 10 runs per day, the H100 saves $13,000/month in developer blocking time versus ~$4,600/month more in compute cost at typical provider rates — a net positive of ~$8,400/month. Not recommended if you run fewer than 5 training jobs per day — the compute cost delta exceeds developer time savings at that frequency, and the A100 produces better overall economics. The H100's throughput advantage (1.58× A100 raw compute per MLPerf Training v3.1) does not justify the premium for low-frequency workloads.
How does the AMD MI300X compare to the H100 for LLM training?
The MI300X and H100 occupy different niches. The MI300X has 192 GB of HBM3 (versus H100's 80 GB HBM3), which allows it to fit a 70B model on a single card instead of two, eliminating tensor parallelism communication overhead and improving throughput by ~18%. For pre-training 70B–130B models, the MI300X offers meaningful efficiency advantages. For fine-tuning smaller models (under 80B), the H100 matches or exceeds MI300X throughput at lower cost — the MI300X's memory advantage provides no benefit when the model fits comfortably on a single 80 GB card. On MLPerf Training v3.1 GPT-3 175B, the MI300X is 17% slower per GPU than H100 despite higher peak TFLOPS (5,220 vs 3,958 FP8 TFLOPS), demonstrating that memory capacity advantages matter less than communication efficiency for large distributed training clusters. Not recommended: MI300X for fine-tuning workloads under 70B parameters — H100 or A100 deliver better throughput-per-dollar at that scale.
When should you use the GB200 for LLM training instead of H100?
The GB200 NVL72 is cost-justified for pre-training foundation models at 405B+ parameters or for organizations running continuous multi-week training runs where wall-clock time directly impacts business timelines. At 20,000 FP8 TFLOPS per GPU and 8 TB/s memory bandwidth, the GB200 is 2.9× more efficient per GPU-minute than H100 for GPT-3 175B training (MLPerf Training v3.1: 605 vs 5,581 GPU-minutes). At $8+/hr per GPU, a 72-GPU NVL72 system costs $580/hr. The GB200 is not cost-effective for fine-tuning, inference, or training runs under 7B–70B parameters — the A100 or H100 produce better ROI for those workloads. Not recommended if you're pre-training under 130B parameters — MI300X delivers comparable memory capacity (192 GB HBM3 vs 192 GB HBM3e) at roughly half the hourly cost. Avoid GB200 for batch sizes under 512 in inference workloads — the NVLink overhead negates throughput gains.
What is the cheapest GPU for LLM inference in 2026?
The A100 80GB at typical provider rates of ~$2.00/hr delivers the lowest cost per million tokens for 7B-13B model inference. On Mistral 7B via vLLM, the A100 generates 12,000 tokens/second at ~$0.046 per million tokens — the lowest rate among production-grade GPU options. For 70B model inference, the H100 actually produces a comparable cost per token (~$0.22/M vs A100's ~$0.23/M) because its higher throughput (3,800 vs 2,400 tokens/sec) offsets the higher hourly rate. The MI300X is cost-competitive for 70B inference (~$0.22/M) and provides single-card serving that eliminates tensor parallelism overhead; MI300X achieves 1.8× A100 throughput on LLaMA-70B per MLCommons Inference v4.0. Not recommended: GB200 for inference-only workloads — it is overkill for any serving scenario under 405B parameters and does not recover its $8+/hr cost on standard batch inference. Not recommended: MI300X for 7B–13B inference — the A100's ~$0.046/M token cost is 4–5× cheaper and the MI300X's memory advantage provides no structural benefit at that model size. Compare live GPU rates across providers on GYGO's marketplace.
How do you calculate GPU ROI for an LLM project?
GPU ROI for LLM projects has two components: compute cost and developer time cost. Compute TCO = GPU_count x price/hr x hours/day x days. Developer time cost = (slower_GPU_time - faster_GPU_time) x developer_hourly_rate x runs/month. The H100 justifies its premium when developer time savings exceed the compute cost delta. For a team doing 10 runs/day at $150/hr developer cost, the H100's 26-minute saving per 70B fine-tuning run produces $13,000/month in recovered developer time versus ~$4,600/month in extra compute cost at typical provider rates on an 8-GPU cluster. For a full buy-versus-rent analysis, H100 ownership breaks even versus rental at approximately 85% sustained utilization over 24 months, depending on provider rates.
Can you fine-tune a 70B LLM on a single GPU?
A 70B LLM can be fine-tuned on a single GPU using QLoRA (4-bit quantized low-rank adaptation), which reduces the 140 GB BF16 memory requirement to approximately 35–45 GB depending on sequence length and LoRA rank. This fits on a single A100 80GB or H100 80GB. The MI300X's 192 GB of HBM3 allows full BF16 fine-tuning of a 70B model on a single card, which is faster than QLoRA at the cost of higher memory usage. For production-quality fine-tuning of 70B models, 2-4 GPUs with ZeRO-3 distributed training produces the best balance of speed, memory efficiency, and output quality.
Which GPU providers offer H100 and A100 rentals at the best price in 2026?
Pricing varies across providers and changes frequently. As of Q1 2026, typical on-demand rates are: RunPod (~$3.19/hr H100, ~$2.29/hr A100), Lambda Labs (~$3.49/hr H100, ~$2.49/hr A100), CoreWeave (~$4.50/hr H100, ~$3.20/hr A100), and Vast.ai (~$2.85/hr H100, ~$2.10/hr A100). Spot and reserved pricing can be significantly lower. GYGO's marketplace lets you compare live rates across all major providers in one place to find the best deal for your workload. For a full GPU marketplace comparison including availability scores and uptime SLAs, see the Vast.ai Alternatives 2026 guide.
More questions about GPU infrastructure? See our full FAQ →
Ready to Start Training Your LLM Without Overpaying?
TL;DR — Get Started
Compare A100 and H100 rates across providers on GYGO before committing. Prices differ by 20–40% across RunPod, Lambda Labs, CoreWeave, and Vast.ai for identical hardware. Start with spot rentals to validate your workload before committing to reserved instances. For sustained training above 85% utilization, hardware ownership may produce better long-term economics than cloud rental.
GYGO’s marketplace lets you compare A100 and H100 rental rates across leading providers — RunPod, Lambda Labs, CoreWeave, Vast.ai and more — so you can find the best price for your workload without checking each provider individually. Start with no minimum commitment, or explore hardware purchasing for sustained workloads where ownership beats rental.
Need to compare GPU hardware specs in more detail? See the 2026 GPU Showdown →