Pascal DatacenterDatacenterPascalPCIe 3CUDA
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 Tesla P100 was NVIDIA's flagship datacenter GPU of the Pascal generation, launched in 2016 as the first accelerator to use HBM2 memory. Its 732 GB/s HBM2 bandwidth was extraordinary at launch and remains respectable for a 10-year-old card. The P100 can run 7B models at Q4 and 3B–4B models at FP16. Available at very low cost on cloud platforms like AWS P2 instances and the used market, it represents accessible compute for students and researchers running small-scale inference workloads. As a pure HPC GPU without INT8 Tensor Cores, it is inefficient for modern quantized inference.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
legacy-datacenterhbm-memorycloud-availableend-of-life
Specifications
Compute
FP1618 TFLOPS
INT836 TOPS
ArchitecturePascal
Memory
VRAM16 GB
Bandwidth732 GB/s
General
FamilyPascal Datacenter
SegmentDatacenter
InterconnectPCIe 3
Compute PlatformCUDA
MSRP$5,999
Key Features
16 GB HBM2 — 732 GB/s bandwidth18 TFLOPS FP16 peak (no Tensor Cores)SXM and PCIe variants available300W TDP (SXM) / 250W (PCIe)CUDA Compute Capability 6.0NVLink 1.0 support on SXM variant
For AI Workloads
Strengths
- HBM2 bandwidth (732 GB/s) is high for its era — decent token generation for 7B Q4 models
- Available at minimal cost on AWS P2 instances and used server market
- 16 GB VRAM handles 7B models at Q4 quantization
- SXM variant supports NVLink for multi-GPU configurations of small models
Considerations
- No Tensor Cores — FP16 and INT8 inference runs on CUDA cores, far slower than modern alternatives
- Cannot run 13B models at any practical quantization — 16 GB is insufficient
- Software framework support may be limited; CUDA Compute 6.0 excluded from some newer libraries
- Hardware is approaching 10 years old — reliability concerns for production inference
Pascal is NVIDIA's first 16nm FinFET GPU architecture, powering the GTX 10-series consumer cards and Tesla P100/P40 datacenter accelerators. It introduced unified memory architecture and NVLink interconnect for datacenter GPUs.
AI Relevance
No dedicated Tensor Cores — all AI inference runs on standard CUDA cores at FP16 or FP32 precision. Still usable for small models (7B Q4) on cards with sufficient VRAM like the GTX 1080 Ti (11 GB) or P40 (24 GB), but significantly slower than Turing and newer.
Process: TSMC 16nmPlatform: CUDAPrecisions: FP32, FP16
Recommendations by Workload
Qwen 3.5 9B 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 84.6 tok/s · 58K ctx · llama.cppEST.
Qwen 3.5 9B 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 84.6 tok/s · 58K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.8 GB
397BTier 100Needs ~246.1 GB
123BTier 100Needs ~80.2 GB
1000BTier 100Needs ~616.2 GB
1000BTier 100Needs ~616.2 GB
Image & Video Generation
Diffusion Model Compatibility
31 of 52 models can generate images or video on your Tesla P100 16GB
Upgrade paths
Upgrade from Tesla P100 16GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
MacBook Pro M3 24GBNext step up
24 GB Unified (+8)
CUnlocks 2 additional models that do not fit on the current setup.Unlocks Qwen 3.6 27B, Gemma 4 26B A4B
Unlocks 2 additional models that do not fit on the current setup.
~$1,099 MSRP
20 GB VRAM (+4)
BUnlocks 14 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+11 more · +8% faster avg
Unlocks 14 additional models that do not fit on the current setup.
~$2,000 MSRP
👁 IntelIntel Arc Pro B60 24GBBest value 24 GB VRAM (+8)
AUnlocks 36 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+33 more
Unlocks 36 additional models that do not fit on the current setup.
~$599 MSRP
AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+272)8000 GB/s (+7268)
BUnlocks 81 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 397B A17B, Devstral 2 123B Instruct+78 more · +152% faster avg
Unlocks 81 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 152%.
~$8,000 MSRP
Frequently Asked Questions
16
GB
Tesla P100 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~23.5s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 46s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~5m 49s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~20.4s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~1m 0s/frame |
Qwen 3.5 9B 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 84.6 tok/s · 58K ctx · llama.cppEST.
Qwen 3.5 9B 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 84.6 tok/s · 58K ctx · llama.cppEST.
Granite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 95.1 tok/s · 56K ctx · llama.cppEST.
14B
13.5 GB
55 tok/s
33K ctx
Image
| MAGI-1Video | 256×256 | ~55.1s/frame | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Should you buy Tesla P100 16GB for local AI?
Usable for local AI with limits
Can run 11 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
What will limit you first
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best upgrade itinerary
Unlocks 2 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 24GB (24.0 GB unified memory) is the next step up.
