VOOZH about

URL: https://willitrunai.com/gpus/h800-80gb

โ‡ฑ AI Models for NVIDIA H800 80GB โ€” What Runs on 80GB VRAM


NVIDIA

NVIDIA H800 80GB

Hopper DatacenterDatacenterHopperSXMCUDA

Operating mode

Choose the operating mode for this hardware

Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

See Full AI Tier List for NVIDIA H800 80GB โ†’

About this GPU for AI

The NVIDIA H800 is a China-export-compliant variant of the H100, retaining the full Hopper compute capability โ€” 80 GB HBM3 and Transformer Engine with FP8 โ€” but with NVLink bandwidth cut to approximately 400 GB/s (down from H100's 900 GB/s) and FP64 performance capped at 1 TFLOPS. For single-GPU LLM inference, H800 performance is essentially identical to H100 SXM, making it highly effective for serving 70B models at FP16. The reduced NVLink bandwidth imposes a penalty for multi-GPU tensor parallelism in large training runs, which is why it was designed to be compliant. Like the A800, it was later banned under October 2023 export controls.

Beyond LLMs

AI Capability Matrix

What AI tasks this GPU can handle โ€” from text generation to image and video creation.

CapabilityStatusRepresentative ModelDetail
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4โ€”
LLM Coding (30B)Runs nativelyQwen 3 30B Q4โ€”
LLM Large (70B)
hbm-memorymassive-vramexport-regulatedhigh-bandwidth

Specifications

Compute
FP16900 TFLOPS
INT81800 TOPS
ArchitectureHopper
Memory
VRAM80 GB
Bandwidth3000 GB/s
General
FamilyHopper Datacenter
SegmentDatacenter
InterconnectSXM
Compute PlatformCUDA
MSRP$30,000

Key Features

80 GB HBM3 โ€” 3,000 GB/s bandwidth (near H100 levels)900 TFLOPS FP16 with sparsity / 1,800 INT8 TOPSFP8 Transformer Engine โ€” comparable single-GPU inference to H100Reduced NVLink: ~400 GB/s (vs. H100's 900 GB/s) to meet export thresholdsFP64 capped at 1 TFLOPS (from 60 TFLOPS on H100)SXM form factor, 700W TDP

For AI Workloads

Strengths
  • Single-GPU inference performance matches H100 SXM โ€” FP8 Transformer Engine fully enabled
  • 3 TB/s HBM3 bandwidth delivers fast token generation for large models
  • 80 GB allows 70B models at FP16 on a single card
  • Widely used in deployed Chinese AI inference infrastructure
Considerations
  • Reduced NVLink (~400 GB/s) degrades multi-GPU scaling efficiency for large training runs
  • Subject to export controls โ€” no longer legally exportable under Oct 2023 BIS rules
  • High cost and niche availability outside China-focused supply chains
  • Now effectively superseded in Chinese AI infrastructure by H20 (higher VRAM) and domestic alternatives

Architecture

Hopper

Hopper is NVIDIA's datacenter-focused architecture succeeding Ampere. Built on TSMC 4N, it introduces the Transformer Engine with automatic FP8/FP16 mixed-precision training, HBM3/HBM3e memory, and NVLink 4.0 for multi-GPU scaling. The H100 flagship delivers up to 3x the AI training performance of A100.

AI Relevance

The Transformer Engine automatically manages FP8 precision for optimal training speed without accuracy loss. With up to 141 GB HBM3e (H200), Hopper GPUs can hold the largest open-weight models entirely in GPU memory, making them the workhorse of AI datacenters.

Process: TSMC 4NPlatform: CUDATensor Cores: Gen 4Precisions: FP64, FP32, TF32, FP16, BF16, FP8, INT8

Recommendations by Workload

Chat

S

Qwen 3 32B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 135.4 tok/s ยท 131K ctx ยท llama.cppEST.
30.4 GB / 80.0 GB VRAM

Coding

S

Qwen3-Coder-Next

This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 164.1 tok/s ยท 244K ctx ยท llama.cppEST.
59.2 GB / 80.0 GB VRAM

Agentic Coding

S

Qwen3-Coder-Next

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Full Model Compatibility

๐Ÿ‘ Alibaba
Qwen3-Coder-Next
S97
80B59.2 GB164 tok/s244K ctx
moe
๐Ÿ‘ Alibaba
Qwen 2.5 VL 72B
S96
72B57.7 GB60 tok/s33K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3.6 35B A3B
S93

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

Image & Video Generation

Diffusion Model Compatibility

51 of 52 models can generate images or video on your NVIDIA H800 80GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512ร—5120msS
Stable Diffusion 1.5Image512ร—768100msS
Realistic Vision v5.1Image512ร—768100msS
DreamShaper 8Image512ร—768100msS
LCM DreamShaper v7

Multi-GPU scaling

NVIDIA H800 80GB โ€” Up to 8ร— via NVLink

Scale out with multiple GPUs for larger models. NVLink provides 400 GB/s inter-GPU bandwidth with 12% overhead.

ConfigEffective memoryModels that fitEst. bandwidth
1ร— NVIDIA80 GB350/3743,000 GB/s
2ร— NVIDIA160 GB359/3745,280 GB/s
4ร— NVIDIA320 GB364/37410,560 GB/s
8ร— NVIDIA640 GB373/37421,120 GB/s

Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.88ร— per additional GPU.

Upgrade paths

Upgrade from NVIDIA H800 80GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

๐Ÿ‘ NVIDIA
8ร— NVIDIA H800 80GBMulti-GPU
8 ร— 80 GB = 640 GB effectivevia NVLink (400 GB/s)
A
Unlocks 23 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Kimi K2.5, Kimi K2.6+20 more ยท +119% faster avg

Unlocks 23 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 119%.

NVLink gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.

~$30,000 MSRP

Mac Studio M2 Ultra 128GBNext step up
128 GB Unified (+48)
B
Unlocks 1 additional models that do not fit on the current setup.Unlocks Mixtral 8x22B

Unlocks 1 additional models that do not fit on the current setup.

~$3,999 MSRP

๐Ÿ‘ NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgrade
96 GB VRAM (+16)
B
Unlocks 1 additional models that do not fit on the current setup.Unlocks Mixtral 8x22B

Unlocks 1 additional models that do not fit on the current setup.

~$9,999 MSRP

AMD Instinct MI325X 256GBBiggest leap
256 GB VRAM (+176)6000 GB/s (+3000)
B
Unlocks 13 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, DeepSeek V4 Flash, Qwen 3 235B A22B+10 more ยท +26% faster avg

Unlocks 13 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 26%.

~$20,000 MSRP

AMD Instinct MI350X 288GBBest value
288 GB VRAM (+208)8000 GB/s (+5000)
B
Unlocks 14 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, DeepSeek V4 Flash, Qwen 3 235B A22B+11 more ยท +41% faster avg

Unlocks 14 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 41%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

NVIDIA H800 80GB vs NVIDIA A100 80GBNVIDIA H800 80GB vs NVIDIA H100 80GBNVIDIA H800 80GB vs NVIDIA A800 80GB
Compare this GPUCompare with another GPU โ†’
80
GB
VRAM
3kGB/s
Bandwidth
900TFLOPS
FP16 Compute
1.8kTOPS
INT8 Inference
$30,000 MSRP
NVIDIA H800 80GBCategory AvgMac Studio M2 Ultra 128GB
Runs natively
Llama 3.1 70B Q4
โ€”
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16~300ms per image
Image Gen (Flux)Runs nativelyFlux.1 Dev FP16~~1.5s per image
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16~~1.9s per image
Video Short (25f)Runs nativelyLTX Video 2B~300ms/frame
Video Long (100f)Runs with offloadWan Video 14B~900ms/frame
Decode 164.1 tok/s ยท 244K ctx ยท llama.cppEST.
60.6 GB / 80.0 GB VRAM

Reasoning

S

Qwen3-Coder-Next

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 164.1 tok/s ยท 244K ctx ยท llama.cppEST.
59.2 GB / 80.0 GB VRAM

RAG

S

Qwen 3.5 27B

This model is a direct match for rag. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 159.3 tok/s ยท 131K ctx ยท llama.cppEST.
31.7 GB / 80.0 GB VRAM
35B
34.4 GB
309 tok/s
194K ctx
+1moe
๐Ÿ‘ Alibaba
Qwen3-Coder 30B A3B Instruct
S93
30.5B29.0 GB367 tok/s256K ctx
moe
๐Ÿ‘ Alibaba
Qwen 3.5 27B
S92
27B28.5 GB159 tok/s131K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3.5 35B A3B
S92
35B31.7 GB336 tok/s131K ctx
moe
๐Ÿ‘ Alibaba
Qwen3-VL 30B A3B Instruct
S92
30B28.7 GB380 tok/s256K ctx
moe
๐Ÿ‘ Alibaba
Qwen 3 32B
S91
32B32.3 GB135 tok/s131K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3.6 27B
S91
27B26.3 GB99 tok/s262K ctx
+1dense
๐Ÿ‘ Cohere
Command A 111B
S91
111B80.5 GB33 tok/s14K ctx
dense
๐Ÿ‘ Mistral
Magistral Small 2507
S90
24B26.0 GB178 tok/s131K ctx
dense
๐Ÿ‘ Mistral
Devstral Small 2 24B Instruct
S90
24B26.0 GB178 tok/s256K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3 30B A3B
S90
30.5B29.0 GB367 tok/s131K ctx
moe
๐Ÿ‘ NVIDIA
Nemotron 3 Nano 30B
S90
30B29.6 GB143 tok/s131K ctx
dense
๐Ÿ‘ Google
Gemma 4 31B
S90
30.7B42.3 GB85 tok/s57K ctx
dense
๐Ÿ‘ Mistral
Devstral Small 1.1
S88
24B26.0 GB178 tok/s131K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3.5 9B
S88
9B16.6 GB126 tok/s131K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3 14B
S88
14B19.9 GB196 tok/s131K ctx
dense
๐Ÿ‘ NVIDIA
Nemotron Cascade 2 30B A3B
S88
30B30.1 GB376 tok/s262K ctx
moe
๐Ÿ‘ Microsoft
Phi-4-reasoning-plus 14B
S87
14.7B20.9 GB206 tok/s33K ctx
dense
๐Ÿ‘ OpenAI
GPT-OSS 20B
S87
21B24.2 GB467 tok/s128K ctx
moe
๐Ÿ‘ Alibaba
Qwen 3 8B
S86
8B16.0 GB112 tok/s131K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3.5 122B A10B
S86
122B85.8 GB74 tok/s4K ctx
moe
๐Ÿ‘ LG AI
EXAONE 4.0 32B
S86
32B32.3 GB134 tok/s131K ctx
dense
๐Ÿ‘ Google
Gemma 4 26B A4B
A84
25.2B27.9 GB395 tok/s243K ctx
moe
๐Ÿ‘ Alibaba
Qwen 3.5 4B
A84
4B13.5 GB56 tok/s131K ctx
dense
๐Ÿ‘ Mistral
Mistral Small 4 119B
A84
119B86.9 GB79 tok/s4K ctx
moe
๐Ÿ‘ Mistral
Devstral 2 123B Instruct
A83
123B89.3 GB25 tok/s4K ctx
dense
๐Ÿ‘ Mistral
Ministral 3 14B
A82
14B19.9 GB196 tok/s262K ctx
multimodal
๐Ÿ‘ NVIDIA
Nemotron Nano 8B
A81
8B15.7 GB112 tok/s131K ctx
dense
๐Ÿ‘ Microsoft
Phi-4 Mini Reasoning 4B
A81
3.8B12.7 GB53 tok/s131K ctx
dense
๐Ÿ‘ OpenAI
GPT-OSS 120B
A81
117B85.2 GB29 tok/s4K ctx
dense
๐Ÿ‘ Mistral AI
Pixtral Large 124B
A79
124B89.9 GB25 tok/s4K ctx
dense
๐Ÿ‘ Mistral
Leanstral 119B A6B
A79
119B90.3 GB68 tok/s4K ctx
moe
๐Ÿ‘ Jina AI
Jina Embeddings v3
A74
0.57B12.0 GB8 tok/s8K ctx
dense
๐Ÿ‘ BAAI
BGE M3
A73
0.57B11.2 GB8 tok/s8K ctx
dense
๐Ÿ‘ Alibaba
Qwen 3.5 397B A17B
F0
397B253.9 GB5 tok/s4K ctx
moe
๐Ÿ‘ Moonshot AI
Kimi K2.5
F0
1000B626.3 GB2 tok/s4K ctx
moe
๐Ÿ‘ Moonshot AI
Kimi K2.6
F0
1000B626.3 GB2 tok/s4K ctx
+1moe
๐Ÿ‘ DeepSeek
DeepSeek V4 Pro
F0
1600B872.8 GB2 tok/s4K ctx
moe
๐Ÿ‘ DeepSeek
DeepSeek V4 Flash
F0
284B168.2 GB13 tok/s4K ctx
moe
๐Ÿ‘ Z.ai
GLM-5.1
F0
754B487.9 GB2 tok/s4K ctx
moe
๐Ÿ‘ Z.ai
GLM-5
F0
744B481.8 GB2 tok/s4K ctx
moe
๐Ÿ‘ DeepSeek
DeepSeek V3.2
F0
671B418.7 GB3 tok/s4K ctx
moe
๐Ÿ‘ Alibaba
Qwen 3 235B A22B
F0
235B155.1 GB14 tok/s4K ctx
moe
๐Ÿ‘ Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B304.6 GB4 tok/s4K ctx
moe
MiniMax M2.7
F0
230B153.0 GB17 tok/s4K ctx
moe
๐Ÿ‘ DeepSeek
DeepSeek Coder V2 236B
F0
236B211.5 GB8 tok/s4K ctx
moe
๐Ÿ‘ DeepSeek
DeepSeek R1 671B
F0
671B477.8 GB3 tok/s4K ctx
moe
๐Ÿ‘ DeepSeek
DeepSeek V3.1 671B
F0
671B477.8 GB3 tok/s4K ctx
moe
Image
512ร—768
0ms
S
PixArt-SigmaImage1024ร—1024300msS
FramePack I2VVideo1280ร—720600ms/frameS
SDXL TurboImage512ร—5120msS
SDXL LightningImage1024ร—1024100msS
Stable Diffusion XL 1.0Image1024ร—1024300msS
Playground v2.5Image1024ร—1024500msS
RealVisXL v5.0Image1024ร—1024400msS
DreamShaper XLImage1024ร—1024400msS
Juggernaut XL v9Image1024ร—1024400msS
Animagine XL 3.1Image1024ร—1024400msS
Pony Diffusion V6 XLImage1024ร—1024400msS
Animagine XL 4.0Image1024ร—1024400msS
Illustrious XLImage1024ร—1024400msS
Wan Video 2.1 1.3BVideo480ร—832200ms/frameS
Stable Diffusion 3.5 MediumImage1024ร—1024600msS
Flux.2 Klein 4BImage1024ร—1024100msS
LTX Video 2BVideo1280ร—720300ms/frameS
KolorsImage1024ร—1024700msS
Stable CascadeImage1024ร—1024900msS
AuraFlow v0.3Image1536ร—1536~1.5sS
Stable Diffusion 3.5 LargeImage1024ร—1024~1.9sS
Stable Diffusion 3.5 Large TurboImage1024ร—1024300msS
CogVideoX 2BVideo720ร—480300ms/frameS
HunyuanVideoVideo720ร—1280600ms/frameS
ChromaImage1024ร—1024300msS
Z-Image TurboImage1536ร—1536400msS
Flux.1 DevImage1024ร—1024~1.5sS
Flux.1 SchnellImage1024ร—1024300msS
LTX Video 13BVideo1280ร—720600ms/frameS
Flux.1 Kontext DevImage1024ร—1024~1.7sS
AnimateDiff v1.5.3Video512ร—768200ms/frameS
Cosmos Diffusion 7BVideo1024ร—576500ms/frameS
CogVideoX 5BVideo720ร—480400ms/frameS
Wan2.2 TI2V 5BVideo832ร—480400ms/frameS
Flux.2 Klein 9BImage1024ร—1024200msS
Flux.1 Fill DevImage1024ร—1024~1.5sS
Mochi 1 PreviewVideo848ร—480600ms/frameS
HunyuanVideo 1.5Video720ร—1280500ms/frameS
Helios 14BVideo1280ร—720600ms/frameS
SkyReels V2 14BVideo1280ร—720600ms/frameS
Wan Video 2.1 14BVideo720ร—1280600ms/frameS
Wan Video 2.2 14BVideo720ร—1280600ms/frameS
Qwen ImageImage1024ร—1024600msS
Qwen Image EditImage1024ร—1024600msS
Flux.2 DevImage1024ร—1024~16.2sS
MAGI-1Video1280ร—720800ms/frameA
HunyuanImage 3.0Image256ร—256~1sF

Image models estimated at 1024ร—1024 (28 steps, FP16). Video models estimated at 768ร—512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.

There are 5 upgrade path(s) from NVIDIA H800 80GB: NVIDIA H800 80GB, Mac Studio M2 Ultra 128GB. Upgrading would unlock larger models and faster inference speeds.

Buying advice

Should you buy NVIDIA H800 80GB for local AI?

Excellent choice for local AI

Runs 36 of 50 top models well โ€” a strong all-rounder for local inference.

80.0 GB

VRAM

$30,000

MSRP

$375/GB

Cost per GB VRAM

Best models for this GPU

What will limit you first

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best upgrade itinerary

Unlocks 1 additional models that do not fit on the current setup.

Want more headroom? Mac Studio M2 Ultra 128GB (128.0 GB unified memory) is the next step up.