NVIDIA H200 141GB
The NVIDIA H200 141GB has 141 GB VRAM and 4800 GB/s memory bandwidth. It can run 82 of our 110 tracked models natively in VRAM at 8k context.
With 141 GB HBM3e, the NVIDIA H200 141GB is a datacenter-tier GPU that can run 82 models natively.
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, 19 tok/s) and GPT-OSS 120B (MoE) at Q6_K (107.93 GB, 174.1 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 19.3 tok/s. Across the 110 models this site tracks, the H200 runs 81 of them fully in VRAM at 8k context, 8 more than the H100 80GB's 73, 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) | 82 / 110 |
| Models (offload) | 4 / 110 |
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 an 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 an H200 141GB from $3.59/hr on RunPod ↗ (+$5 signup credit)Prices as of Oct 17, 2026. Rates change; check RunPod for current pricing.
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 (82)
- DeepSeek V4 Flash 284B284B · MMLU-Pro 86.3Q2_K · ~150.4 t/s
- DeepSeek V4 Flash 0731 284B284B · MMLU-Pro N/AUD-IQ3_XXS · ~153.2 t/s
- Qwen3 235B-A22B (MoE)235B · MMLU-Pro 84.4Q3_K_M · ~65.5 t/s
- MiniMax M2.5 229B229B · MMLU-Pro 84.8Q3_K_M · ~106.8 t/s
- MiniMax M2.7 229B229B · MMLU-Pro 86.0Q3_K_M · ~106.8 t/s
Show 77 more
- Step 3.7 Flash198B · MMLU-Pro N/AQ3_K_M · ~126.3 t/s
- Step 3.5 Flash196.81B · MMLU-Pro 84.4Q3_K_M · ~126.3 t/s
- Qwen3.8-Flash-Next180B · MMLU-Pro N/AQ4_K_M · ~157.9 t/s
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q6_K · ~39 t/s
- Mistral Medium 3.5 128B128B · MMLU-Pro N/AQ6_K · ~13.8 t/s
- Ornith 1.5 122B (MoE)124.6B · MMLU-Pro N/AQ6_K · ~109.1 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q6_K · ~109.1 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q6_K · ~70.1 t/s
- Nemotron 3.5 Super VL 120B-A12B120B · MMLU-Pro N/AQ6_K · ~74.7 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q6_K · ~174.1 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q8_0 · ~51.4 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q8_0 · ~79 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q8_0 · ~79 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q8_0 · ~18.5 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q8_0 · ~19 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q8_0 · ~19 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q8_0 · ~19 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7BF16 · ~51.8 t/s
- Flint 40B (MoE)39.8B · MMLU-Pro N/ABF16 · ~137.1 t/s
- Command-R 35B35B · MMLU-Pro 33.0BF16 · ~19 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3BF16 · ~140.6 t/s
- Qwen 3.6 35B-A3B (MoE)35B · MMLU-Pro 85.2BF16 · ~140.6 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/ABF16 · ~140.6 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0BF16 · ~21 t/s
- Nanbeige4.3 33B33.1B · MMLU-Pro N/ABF16 · ~22.1 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5BF16 · ~22 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0BF16 · ~21.9 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3BF16 · ~21.9 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0BF16 · ~21.9 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3BF16 · ~124.6 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2BF16 · ~23.4 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5BF16 · ~122.5 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6BF16 · ~119.2 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/ABF16 · ~26.5 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0BF16 · ~26 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5BF16 · ~26.8 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2BF16 · ~26.5 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2BF16 · ~26.5 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~115.6 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~127.6 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/ABF16 · ~26.5 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6BF16 · ~134.8 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~30.2 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~31.1 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~152.2 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~46.2 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~45.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~48 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~54.4 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~54.5 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~55.3 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~63.7 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~75.4 t/s
- Bonsai 2 9B9B · MMLU-Pro N/ATernary (Q2_0) · ~271.9 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~75.4 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~79.8 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~79.8 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~77.8 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~87.6 t/s
- Gemma 4 E4B7.52B · MMLU-Pro 69.4BF16 · ~121.8 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~86.2 t/s
- Gemma 4 E2B4.65B · MMLU-Pro 60.0BF16 · ~200.3 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~141.2 t/s
- Bonsai 2 4B4B · MMLU-Pro N/ATernary (Q2_0) · ~352.3 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~115.7 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~136.8 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~160.2 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~161.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~187.5 t/s
- Bonsai 2 2B2B · MMLU-Pro N/ATernary (Q2_0) · ~542.2 t/s
- LFM3 1.7B1.73B · MMLU-Pro N/ABF16 · ~273.7 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~219.6 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~271 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~374.8 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~332.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~470.8 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~388.2 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 (24)
- 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
- Kimi K3.1 Pro
- Qwen3.8 2.4T-A95B
- DeepSeek V4 Pro 0813 1.6T
- Muse Spark Open 816B-A42B
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- DeepSeek V4.1 Flash 552B
- DeepSeek V4.1 Flash 1017 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 82 of the 110 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, Nemotron 3.5 Super VL 120B-A12B at Q6_K.
- 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 23.4 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 26.5 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 77.8 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. Rentals start from $3.59/hr on RunPod (as of Oct 17, 2026). This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.