NVIDIA · Blackwell · 16 GB VRAM tier
Best AI Models for NVIDIA RTX PRO 4000 Blackwell Generation Laptop GPU (16 GB)
Runs 14B models well; 27–31B only just fits.
16 GB GDDR7896 GB/s167 TFLOPS FP16175 WBlackwell 2025
16 GB at 896 GB/s with ECC: ~14B at Q4-Q6, fast decode.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s | 91.6 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 179 t/s | 179 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 173 t/s | 173 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 61.2 t/s | 61.2 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s | 70.8 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 173 t/s | 173 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 110 t/s | 110 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 91.6 t/s | 91.6 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s | 70.8 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 93.8 t/s | 93.8 tok/s | Q4_K_M · 6.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s | 91.6 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 173 t/s | 173 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 106 t/s | 106 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 110 t/s | 110 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s | 70.8 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s | 91.6 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 179 t/s | 179 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 72.2 t/s | 72.2 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 110 t/s | 110 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s | 70.8 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 179 t/s | 179 tok/s | Q4_K_M · 3.4 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 173 t/s | 173 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 91.6 t/s | 91.6 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s | 70.8 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 61.2 t/s | 61.2 tok/s | Q4_K_M · 12.5 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s | 91.6 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 179 t/s | 179 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 72.4 t/s | 72.4 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 205 t/s | 205 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 93.8 t/s | 93.8 tok/s | Q4_K_M · 6.5 GB |
See all 309 models on the RTX PRO 4000 Blackwell Generation Laptop GPU →Best quant that fits · speeds estimated on this card
The RTX PRO 4000 Blackwell Generation Laptop GPU runs 14B models well. Blackwell generation, released 2025.
GenerationBlackwellreleased 2025
RTX PRO 4000 Blackwell Generation Laptop GPU strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 174 tok/s in 15.7 GB
- Reads long prompts fast: A 4K-token prompt on Qwen3.5 9B starts answering in 1.1 s
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 16 GB here
Output speed depends on memory bandwidth (896 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB91.6tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB61.3tok/s
- 38.9tok/s
- 174tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 1073 models on the RTX PRO 4000 Blackwell Generation Laptop GPU
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
| Q4_K_M | 6.2 GB | 108.1 t/s | 8K | S93, Runs great | ||
Pantheon Reasoning 27BChatRoleplayReasoning | Q3_K_L | 15.4 GB | 38.9 t/s | 262K | B48, Decent | |
| Q4_K_M | 1.7 GB | 455.0 t/s | 33K | S100, Runs great | ||
TinyStories 1MChat | BF16 | 0.0 GB | 58240.0 t/s | 2K | S100, Runs great | |
Opt 350MChat | FP16 | 0.8 GB | 756.4 t/s | 2K | S100, Runs great | |
| Q4_K_M | 8.6 GB | 98.0 t/s | — | S91, Runs great | ||
Granite 3.1 3B A800m InstructChatFunctions | Q4_K_M | 3.4 GB | 348.7 t/s | 131K | S100, Runs great | |
| Q4_K_M | 5.7 GB | 118.6 t/s | 8K | S95, Runs great | ||
Mamba 2.8B HFChat | Q4_K_M | 3.7 GB | 318.3 t/s | — | S100, Runs great | |
| Q4_K_M | 2.9 GB | 336.6 t/s | 8K | S100, Runs great | ||
MiniCPM MoE 8x2BChat | Q4_K_M | 11.4 GB | 104.5 t/s | 4K | S92, Runs great | |
Gpt2 MediumChat | Q4_K_M | 0.3 GB | 2329.6 t/s | 1K | S100, Runs great | |
LFM2.5 230M BaseChat | Q4_K_M | 0.6 GB | 1164.8 t/s | 128K | S100, Runs great | |
Apertus 8B Instruct 2509ChatFunctions | Q4_K_M | 7.3 GB | 107.9 t/s | 66K | S93, Runs great | |
| Q4_K_M | 0.1 GB | 4160.0 t/s | 2K | S100, Runs great |
Specs
- Architecture
- Blackwell
- VRAM
- 16 GB GDDR7
- Memory bandwidth
- 896.0 GB/s
- FP16 (dense)
- 166.75 TFLOPS
- CUDA cores
- 7,680
- Tensor cores
- 240
- Compute capability
- 12.0
- TDP
- 175 W
- Released
- 2025-03-18
RTX PRO 4000 Blackwell Generation Laptop GPU FAQ
What is the largest model the RTX PRO 4000 Blackwell Generation Laptop GPU can run?
Gemma 4 26B A4B IT (25.8B) is the largest at 4-bit or better: at Q4_K_S it uses 15.5 GB of 16 GB, and runs at about 164 tok/s. At 3-bit, a bigger model fits too: Gemma 2 27B IT (27.2B) at IQ3_M, 15.0 GB.
Can the RTX PRO 4000 Blackwell Generation Laptop GPU run Qwen3.5 9B?
Yes. Qwen3.5 9B fits at Q4_K_M, using 6.6 GB of 16 GB, and it is the best model that runs well on this card.
Can the RTX PRO 4000 Blackwell Generation Laptop GPU run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 16 GB on this card.
How many tokens per second does the RTX PRO 4000 Blackwell Generation Laptop GPU give?
About 91.6 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 1.1 s on a 4K-token prompt.
What power supply does the RTX PRO 4000 Blackwell Generation Laptop GPU need?
Plan a 350 W PSU (2× the 175 W TDP).
Is 16 GB enough for coding and agents?
Yes for coding: GPT OSS 20B, the best of the 2 coding models that fit, runs at about 173 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.