NVIDIA H200 141GB
The NVIDIA H200 141GB has 141 GB VRAM and 4800 GB/s memory bandwidth. It can run 74 of our 99 tracked models natively in VRAM at 8k context.
With 141 GB HBM3e, the NVIDIA H200 141GB is a datacenter-tier GPU that can run 74 models natively. This site's calculator puts Llama 3.3 70B at Q8_0 (86.35 GB, 40.5 tok/s) and GPT-OSS 120B (MoE) at Q6_K (107.93 GB, 218.7 tok/s), both fit natively in the 141GB pool. GLM 4.5 (355B, MoE) is the real ceiling: even Q2_K needs CPU offload at 154.94 GB, decoding at 14.5 tok/s. Across the 99 models this site tracks, the H200 runs 74 of them fully in VRAM at 8k context, 8 more than the H100 80GB's 66, purely from the extra memory headroom.
NVIDIA H200 141GB: Announced November 13, 2023 at SC23 and shipping since Q2 2024, the H200 keeps the H100's GH100 Hopper die but swaps in 141GB of HBM3e at 4.8 TB/s, nearly double the H100's 80GB capacity and 1.4x its 3,350 GB/s bandwidth (NVIDIA's own comparison figures). Ships as an SXM module (up to 700W) or a PCIe-based NVL card (up to 600W).
This site's calculator puts Llama 3.3 70B at Q8_0 (86.35 GB, 40.5 tok/s) and GPT-OSS 120B (MoE) at Q6_K (107.93 GB, 218.7 tok/s), both fit natively in the 141GB pool. GLM 4.5 (355B, MoE) is the real ceiling: even Q2_K needs CPU offload at 154.94 GB, decoding at 14.5 tok/s. Across the 99 models this site tracks, the H200 runs 74 of them fully in VRAM at 8k context, 8 more than the H100 80GB's 66, purely from the extra memory headroom.
Full CUDA support from day one: same GH100 die and sm_90 compute capability as the H100, so no toolkit or driver upgrade is needed to recognize it, though server vendors typically ship a BIOS/firmware update for the higher-capacity HBM3e stacks. vLLM and TensorRT-LLM both treat it as a drop-in H100 replacement with more headroom for KV cache and larger batches. Cloud-only for nearly everyone; available on AWS, Azure, GCP, OCI, CoreWeave, Lambda, and other GPU clouds by the hour.
| Vendor | NVIDIA |
| Architecture | Hopper |
| VRAM | 141 GB |
| Memory type | HBM3e |
| Memory bandwidth | 4800 GB/s |
| Compute backend | CUDA |
| Tier | Datacenter |
| Released | 2024 |
| Models (native) | 74 / 99 |
| Models (offload) | 4 / 99 |
Memory capacity and bandwidth don't climb together, generation to generation
Plotting NVIDIA's last four datacenter flagships by VRAM and bandwidth together shows how unevenly the two numbers actually move; there's no single "the generational upgrade" pattern:
H100 to H200 (this page) is a capacity-led jump: 80GB to 141GB, up 76%, on a smaller 43% bandwidth gain (3,350 to 4,800 GB/s). The next step, H200 to B200, flips that: capacity rises a smaller 28% but bandwidth jumps 67% to 8,000 GB/s. B200 to B300 flips again: 60% more capacity (180GB to 288GB) at the exact same 8,000 GB/s bandwidth. Each step trades differently between how much a GPU can hold and how fast it can read it, so "newer generation" alone doesn't tell you whether a model that didn't fit the last one now will, or whether one that already fit will just run faster.
Cloud GPU Rental
Don't want to buy a NVIDIA H200 141GB? RunPod is a cloud GPU rental service: rent one by the hour instead, no contract, no upfront hardware cost.
Pay by the hour · no contract · pods start in about a minute.
Rent a NVIDIA H200 141GB on RunPod ↗ (+$5 signup credit)Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.
Popular models for this GPU
Models this GPU runs natively in VRAM (74)
- DeepSeek V4 Flash 284B284B · MMLU-Pro 86.3Q2_K · ~188.2 t/s
- DeepSeek V4 Flash 0731 284B284B · MMLU-Pro N/AUD-IQ3_XXS · ~195.7 t/s
- Qwen3 235B-A22B (MoE)235B · MMLU-Pro 84.4Q3_K_M · ~84.7 t/s
- MiniMax M2.5 229B229B · MMLU-Pro 84.8Q3_K_M · ~172.2 t/s
- MiniMax M2.7 229B229B · MMLU-Pro 86.0Q3_K_M · ~172.2 t/s
Show 69 more
- Step 3.7 Flash198B · MMLU-Pro N/AQ3_K_M · ~163 t/s
- Step 3.5 Flash196.81B · MMLU-Pro 84.4Q3_K_M · ~163 t/s
- Qwen3.8-Flash-Next180B · MMLU-Pro N/AQ4_K_M · ~252 t/s
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q6_K · ~28.7 t/s
- Mistral Medium 3.5 128B128B · MMLU-Pro N/AQ6_K · ~28.9 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q6_K · ~113.2 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q6_K · ~92.9 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q6_K · ~218.7 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q8_0 · ~49.6 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q8_0 · ~70.8 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q8_0 · ~70.8 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q8_0 · ~39.4 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q8_0 · ~40.5 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q8_0 · ~40.5 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q8_0 · ~40.5 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7BF16 · ~35.8 t/s
- Command-R 35B35B · MMLU-Pro 33.0BF16 · ~38.6 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3BF16 · ~154.7 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2BF16 · ~43.2 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/ABF16 · ~154.7 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0BF16 · ~44.1 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5BF16 · ~46.6 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0BF16 · ~46.5 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3BF16 · ~46.5 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0BF16 · ~46.5 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3BF16 · ~152.7 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2BF16 · ~49.6 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5BF16 · ~150 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6BF16 · ~155.6 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/ABF16 · ~55.9 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0BF16 · ~54.3 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5BF16 · ~56.2 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2BF16 · ~57.2 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2BF16 · ~57.2 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~403.3 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~484.7 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/ABF16 · ~57.2 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6BF16 · ~121.3 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~63.2 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~67.4 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~128.9 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~100.8 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~100.6 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~106.3 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~121.2 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~122.6 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~114.6 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~147 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~170.8 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~170.8 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~182.7 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~182.7 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~181.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~199.1 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~200.3 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~366.9 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~346.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~288.3 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~359.7 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~425.1 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~479.9 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~513.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~708.7 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~622.7 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~964.5 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~1135.2 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~1340.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~2834.7 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~2955.8 t/s
Models that fit with CPU offload (4)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
Too large for this GPU (21)
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- MiniMax M1 456B
- GLM-5 744B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- Qwen3.8 2.4T-A95B
- DeepSeek V4 Pro 0813 1.6T
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- DeepSeek V4.1 Flash 552B
Frequently asked questions
- How much VRAM does the NVIDIA H200 141GB have?
- The NVIDIA H200 141GB has 141 GB of HBM3e with 4800 GB/s memory bandwidth.
- What is the NVIDIA H200 141GB best for?
- With 141 GB of VRAM, the NVIDIA H200 141GB is a server-class GPU that runs 70B-class dense models and large MoE models natively, with plenty of room for long context.
- What LLMs can the NVIDIA H200 141GB run locally?
- The NVIDIA H200 141GB can run 74 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen3.8-Flash-Next at Q4_K_M, DeepSeek V4 Flash 0731 284B at UD-IQ3_XXS, Qwen 3.8 27B at BF16.
- Can the NVIDIA H200 141GB run Gemma 4 31B?
- Yes. The NVIDIA H200 141GB runs Gemma 4 31B natively in VRAM at BF16 quantization, achieving approximately 49.6 tokens per second.
- Can the NVIDIA H200 141GB run Qwen 3.6 27B?
- Yes. The NVIDIA H200 141GB runs Qwen 3.6 27B natively in VRAM at BF16 quantization, achieving approximately 57.2 tokens per second.
- Can the NVIDIA H200 141GB run Qwen3 8B?
- Yes. The NVIDIA H200 141GB runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 181.3 tokens per second.
- Can I rent the NVIDIA H200 141GB instead of buying it?
- Yes: RunPod and similar cloud GPU providers let you rent NVIDIA H200 141GB instances by the hour, with no long-term contract. This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.