NVIDIA · Ampere · 16 GB VRAM tier
Best AI Models for NVIDIA RTX A5000 Laptop GPU (16 GB)
Runs 14B models well; 27–31B only just fits.
16 GB at 448 GB/s: ~13-14B Q4 models. Ampere, so no FP8 support.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 30.6 t/s | 30.6 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 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 · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 54.9 t/s | 54.9 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 221 t/s | 221 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 52.8 t/s | 52.8 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 54.9 t/s | 54.9 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 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 · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 36.1 t/s | 36.1 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 54.9 t/s | 54.9 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 30.6 t/s | 30.6 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 · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 36.2 t/s | 36.2 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 103 t/s | 103 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 46.9 t/s | 46.9 tok/s | Q4_K_M · 6.5 GB |
The RTX A5000 Laptop GPU runs 14B models well. Ampere generation, released 2021.
RTX A5000 Laptop GPU strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 108 tok/s in 15.7 GB
- Reads long prompts fast: A 4K-token prompt on Qwen3.5 9B starts answering in 2.0 s
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 16 GB here
- No FP8 or NVFP4: Ampere predates both; use GGUF, AWQ or GPTQ builds
Output speed depends on memory bandwidth (448 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB45.8tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB30.7tok/s
- 19.4tok/s
- 108tok/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 A5000 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 |
|---|---|---|---|---|---|---|
Qwen2.5 7B Instruct 1MChatFunctions | Q4_K_M | 5.8 GB | 58.4 t/s | 1010K | A83, Runs well | |
WildguardChat | Q4_K_M | 7.3 GB | 60.9 t/s | — | A84, Runs well | |
Medgemma 4B ITVision | Q4_K_M | 5 GB | 102.5 t/s | — | S92, Runs great | |
Mistral Small 3.2 24B QiskitChatCode | Q3_K_M | 15.4 GB | 23.4 t/s | 131K | C39, Usable | |
| Q4_K_M | 14.2 GB | 133.8 t/s | 131K | A83, Runs well | ||
Qwen3 4B BaseChat | Q4_K_M | 4.2 GB | 100.4 t/s | 33K | S91, Runs great | |
Glm 4 9B ChatChatFunctions | Q4_K_M | 8.9 GB | 47.0 t/s | 131K | A78, Runs well | |
GLM 4 9B 0414Chat | Q4_K_M | 6.6 GB | 48.4 t/s | 33K | A79, Runs well | |
| Q4_K_M | 6.9 GB | 65.4 t/s | — | S85, Runs great | ||
Gemma 7BChat | Q4_K_M | 8.2 GB | 51.7 t/s | — | A80, Runs well | |
| Q4_K_M | 7.9 GB | 54.9 t/s | — | A82, Runs well | ||
Qwen 7BChat | Q4_K_M | 7.6 GB | 57.1 t/s | 33K | A82, Runs well | |
Deepseek Coder 1.3B KexerChatCode | Q4_K_M | 4.3 GB | 192.8 t/s | 16K | S99, Runs great | |
| Q2_K | 14.3 GB | 128.1 t/s | 131K | A83, Runs well | ||
Qwen3.5 35B A3BVision | Q2_K | 15.7 GB | 107.5 t/s | 262K | C44, Usable |
Specs
- Architecture
- Ampere
- VRAM
- 16 GB GDDR6
- Memory bandwidth
- 448.0 GB/s
- FP16 (dense)
- 86.80 TFLOPS
- CUDA cores
- 6,144
- Tensor cores
- 192
- Compute capability
- 8.6
- TDP
- 165 W
- Released
- 2021-04-12
RTX A5000 Laptop GPU FAQ
What is the largest model the RTX A5000 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 95.1 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 A5000 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 A5000 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 A5000 Laptop GPU give?
About 45.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 2.0 s on a 4K-token prompt.
What power supply does the RTX A5000 Laptop GPU need?
Plan a 350 W PSU (2× the 165 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 98 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.