Best overallGood fit
Qwen2.5 72B Instruct
Frontier-adjacent open weights. Needs a workstation, a multi-GPU rig or a large unified-memory Mac.
- Params
- 72.7B
- Quant
- Q4_K_M
- Memory
- 45 GB
Reference
Models that fit in 64 GB of VRAM
A 64 GB card has about 63 GB usable once the driver and desktop take their share. Every model below is sized against that budget, including the KV cache for a working context.
29 models fit63 GB usableEstimated
Top picks
Best on 64 GB VRAM
Runner-upGood fit
Llama 3.3 70B Instruct
Delivers close to Llama 3.1 405B quality at a size a dual-GPU workstation or 64 GB Mac can actually hold.
- Params
- 70.6B
- Quant
- Q4_K_M
- Memory
- 44 GB
ThirdExcellent fit
Qwen2.5 Coder 32B Instruct
The strongest open code model that still fits one 24 GB card. The reason a lot of people buy a 4090.
- Params
- 32.8B
- Quant
- Q8_0
- Memory
- 36 GB
Full results
29 models
Estimated
| # | Model | Params | Licence | Quantization | Memory | Fit | Fine-tune |
|---|---|---|---|---|---|---|---|
| 1 | Qwen2.5 72B Instruct | 72.7B | Qwen License | Q4_K_M | 45 GB | Good fit | Yes |
| 2 | Llama 3.3 70B Instruct | 70.6B | Llama 3.3 Community License | Q4_K_M | 44 GB | Good fit | Yes |
| 3 | Qwen2.5 Coder 32B Instruct | 32.8B | Apache 2.0 | Q8_0 | 36 GB | Excellent fit | Yes |
| 4 | Qwen2.5 32B Instruct | 32.8B | Apache 2.0 | Q8_0 | 36 GB | Excellent fit | Yes |
| 5 | Mixtral 8x7B Instruct | 46.7B | Apache 2.0 | Q6_K | 38 GB | Excellent fit | Yes |
| 6 | DeepSeek-R1-Distill-Qwen-32B | 32.8B | MIT | Q8_0 | 36 GB | Excellent fit | Yes |
| 7 | Gemma 3 27B Instruct | 27.4B | Gemma Terms of Use | Q8_0 | 33 GB | Excellent fit | Yes |
| 8 | Phi-4 14B | 14.7B | MIT | F16 | 30 GB | Excellent fit | Yes |
| 9 | Mistral Small 24B Instruct | 23.6B | Apache 2.0 | Q8_0 | 26 GB | Excellent fit | Yes |
| 10 | Qwen3 30B-A3B | 30.5B | Apache 2.0 | Q8_0 | 32 GB | Excellent fit | Yes |
| 11 | Qwen2.5 14B Instruct | 14.8B | Apache 2.0 | F16 | 30 GB | Excellent fit | Yes |
| 12 | StarCoder2 15B | 16B | BigCode OpenRAIL-M | F16 | 31 GB | Excellent fit | Yes |
| 13 | Qwen3 14B | 14.8B | Apache 2.0 | F16 | 30 GB | Excellent fit | Yes |
| 14 | DeepSeek-R1-Distill-Qwen-14B | 14.8B | MIT | F16 | 30 GB | Excellent fit | Yes |
| 15 | Mistral Nemo 12B Instruct | 12.2B | Apache 2.0 | F16 | 25 GB | Excellent fit | Yes |
| 16 | Qwen2.5 Coder 7B Instruct | 7.6B | Apache 2.0 | F16 | 15 GB | Excellent fit | Yes |
| 17 | Gemma 3 12B Instruct | 12.2B | Gemma Terms of Use | F16 | 26 GB | Excellent fit | Yes |
| 18 | DeepSeek-Coder-V2-Lite Instruct | 15.7B | DeepSeek License | F16 | 32 GB | Excellent fit | Yes |
| 19 | Gemma 2 9B Instruct | 9.2B | Gemma Terms of Use | F16 | 20 GB | Excellent fit | Yes |
| 20 | Qwen2.5 7B Instruct | 7.6B | Apache 2.0 | F16 | 15 GB | Excellent fit | Yes |
| 21 | Llama 3.1 8B Instruct | 8B | Llama 3.1 Community License | F16 | 16 GB | Excellent fit | Yes |
| 22 | Qwen3 8B | 8.2B | Apache 2.0 | F16 | 17 GB | Excellent fit | Yes |
| 23 | Mistral 7B Instruct v0.3 | 7.25B | Apache 2.0 | F16 | 15 GB | Excellent fit | Yes |
| 24 | Code Llama 7B Instruct | 6.7B | Llama 2 Community License | F16 | 17 GB | Excellent fit | Yes |
| 25 | Phi-3.5 Mini Instruct | 3.8B | MIT | F16 | 11 GB | Excellent fit | Yes |
| 26 | Gemma 3 4B Instruct | 4.3B | Gemma Terms of Use | F16 | 9.9 GB | Excellent fit | Yes |
| 27 | Llama 3.2 3B Instruct | 3.2B | Llama 3.2 Community License | F16 | 7.3 GB | Excellent fit | Yes |
| 28 | SmolLM2 1.7B Instruct | 1.7B | Apache 2.0 | F16 | 5.2 GB | Excellent fit | Yes |
| 29 | Llama 3.2 1B Instruct | 1.24B | Llama 3.2 Community License | F16 | 3.0 GB | Excellent fit | Yes |
How this page is produced
Each model is sized at every quantization it publishes: weights at the true bits-per-weight, KV cache for the context window, plus runtime overhead. The recommendation is the highest quality that still leaves room for a real context.
- · Token rates are omitted here: a capacity is not a specific card, and generation speed depends on memory bandwidth rather than size.
- · For a real machine, use a hardware page or detect your own in the workspace.
- · 29 of these can also be fine-tuned on this hardware.
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