Ampere DatacenterDatacenterAmpereSXMCUDA
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.
About this GPU for AI
The NVIDIA A800 is the China-export-compliant version of the A100 SXM, with NVLink interconnect bandwidth reduced from 600 GB/s to 400 GB/s to comply with U.S. export regulations that were in effect at launch. Core compute performance โ 312 TFLOPS FP16 and 80 GB HBM2e at 1,935 GB/s โ is essentially identical to the A100 80GB, making it fully capable for LLM training and inference. It was widely deployed in Chinese AI clusters, powering training runs for several frontier Chinese LLMs, before being subsequently banned under tightened 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.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | โ |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | โ |
| LLM Large (70B) |
hbm-memorymassive-vramexport-regulateddatacenter-grade
Specifications
Compute
FP16312 TFLOPS
INT8624 TOPS
ArchitectureAmpere
Memory
VRAM80 GB
Bandwidth1935 GB/s
General
FamilyAmpere Datacenter
SegmentDatacenter
InterconnectSXM
Compute PlatformCUDA
MSRP$15,000
Key Features
80 GB HBM2e โ 1,935 GB/s bandwidth (near-identical to A100)312 TFLOPS FP16 with sparsity / 624 INT8 TOPSSXM form factor with reduced NVLink (400 GB/s vs. A100's 600 GB/s)MIG support: up to 7 isolated instances400W TDPExport-regulated: now banned for new export to China under October 2023 BIS rules
For AI Workloads
Strengths
- 80 GB HBM2e enables 70B models at FP16 without quantization โ same as A100
- Core compute performance matches A100 80GB for training and inference workloads
- MIG partitioning supports multi-tenant inference deployments
- Widely deployed in existing Chinese AI infrastructure โ strong in-region availability
Considerations
- NVLink bandwidth reduced to 400 GB/s โ multi-GPU scaling efficiency lower than A100 at large model sizes
- No FP8 support โ trails Ada and Hopper architectures for modern quantized inference
- Subject to complex export licensing; no longer legally exportable to China
- Being displaced by H800 and H20 in Chinese data centers; limited expansion of installed base
Ampere is NVIDIA's second-generation RTX architecture, built on Samsung's 8nm process. It introduced 3rd-generation Tensor Cores with support for sparsity-accelerated INT8 operations and improved FP16 throughput over Turing.
AI Relevance
Sparsity-aware Tensor Cores can effectively double throughput for structured sparse workloads. However, the lack of FP8 support means quantized inference is less efficient than Ada Lovelace or Blackwell.
Process: Samsung 8nmPlatform: CUDATensor Cores: Gen 3Precisions: FP32, FP16, BF16, INT8, INT4
Recommendations by Workload
Qwen 3 32B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 52.7 tok/s ยท 131K ctx ยท llama.cppEST.
Qwen3-Coder-Next is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 101.9 tok/s ยท 244K ctx ยท llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
397BTier 100Needs ~252.5 GB
Also runs on 4ร your GPU via NVLink โ 72 tok/s
1000BTier 100Needs ~622.6 GB
Also runs on 8ร your GPU via NVLink โ 45 tok/s
1000BTier 100Needs ~622.6 GB
Also runs on 8ร your GPU via NVLink โ 45 tok/s
1600BTier 100Needs ~871.8 GB
284BTier 98Needs ~167.6 GB
Also runs on 4ร your GPU via NVLink โ 115 tok/s
Image & Video Generation
Diffusion Model Compatibility
51 of 52 models can generate images or video on your NVIDIA A800 80GB
Multi-GPU scaling
NVIDIA A800 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.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|
| 1ร NVIDIA | 80 GB | 350/374 | 1,935 GB/s |
| 2ร NVIDIA | 160 GB | 359/374 | 3,406 GB/s |
| 4ร NVIDIA | 320 GB | 364/374 | 6,811 GB/s |
| 8ร NVIDIA | 640 GB | 373/374 | 13,622 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 A800 80GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
8 ร 80 GB = 640 GB effectivevia NVLink (400 GB/s)
AUnlocks 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 +112% faster avg
Unlocks 23 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 112%.
NVLink gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.
~$15,000 MSRP
Mac Studio M2 Ultra 128GBNext step up
128 GB Unified (+48)
BUnlocks 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
๐ NVIDIARTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgrade 96 GB VRAM (+16)
BUnlocks 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 (+4065)
BUnlocks 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 +50% faster avg
Unlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 50%.
~$20,000 MSRP
AMD Instinct MI350X 288GBBest value
288 GB VRAM (+208)8000 GB/s (+6065)
BUnlocks 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 +68% faster avg
Unlocks 14 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 68%.
~$8,000 MSRP
Frequently Asked Questions
80
GB
NVIDIA A800 80GBCategory AvgMac Studio M2 Ultra 128GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~1s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~4.6s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~5.6s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~900ms/frame |
| Video Long (100f) | Runs with offload | Wan Video 14B | ~~2.6s/frame |
Qwen3-Coder-Next is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 101.9 tok/s ยท 244K ctx ยท llama.cppEST.
Qwen3-Coder-Next matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 101.9 tok/s ยท 244K ctx ยท llama.cppEST.
Qwen 3.5 27B matches RAG and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 62.1 tok/s ยท 131K ctx ยท llama.cppEST.
35B
34.4 GB
192 tok/s
194K ctx
Image
| MAGI-1Video | 1280ร720 | ~2.4s/frame | A |
Image models estimated at 1024ร1024 (28 steps, FP16). Video models estimated at 768ร512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Should you buy NVIDIA A800 80GB for local AI?
Excellent choice for local AI
Runs 36 of 50 top models well โ a strong all-rounder for local inference.
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.