What can you run with 64GB of RAM?
With 64GB RAM you can run 15 of the 30 open-source models we track — up to about 81B parameters at Q4 quantization. The most popular that fits is Qwen3.5-9B, needing 6.8 GB and generating around ~10 tok/s on typical hardware of this size.
Typical machines: MacBook Pro M4 Max · Mac Studio · High-end desktop. After the operating system takes its share, about 64 GB is free for a model.
Having the memory is not the same as having the speed
64GB RAM decides whether a model loads. How fast it answers is decided by memory bandwidth — how quickly your machine can read the model, which it must do once for every word it writes. Machines with 64GB RAM span 90–546 GB/s, so the fastest is about 6.1× quicker than the slowest with identical capacity. The table quotes a DDR5-5600; pick yours below to see the difference. Why bandwidth and not the processor →
Models that run on 64GB RAM in 2026
| Model | Size | Memory | Est. speed | Feels like |
|---|---|---|---|---|
| Qwen3.5-9BAlibaba / Qwen | 9.7B | 6.8 GBRuns well | ~10 tok/s | Comfortable |
| gpt-oss-20bOpenAI · MoE | 22B | 13 GBRuns well | ~25 tok/s4.3B active | Fast |
| Qwen3.5-4BAlibaba / Qwen | 4.7B | 3.8 GBRuns well | ~21 tok/s | Comfortable |
| Qwen3.5-0.8BAlibaba / Qwen | 873M | 1.3 GBRuns well | ~110 tok/s | Fast |
| Qwen3.5-27BAlibaba / Qwen | 28B | 19 GBRuns well | ~3.5 tok/s | Slow |
| Qwen3.5-35B-A3BAlibaba / Qwen · MoE | 36B | 25 GBRuns well | ~13 tok/s7.2B active | Comfortable |
| Qwen3.5-2BAlibaba / Qwen | 2.3B | 2.0 GBRuns well | ~46 tok/s | Fast |
| GLM-4.7-FlashZhipu AI | 31B | 22 GBRuns well | ~3.1 tok/s | Slow |
| Olmo-3-7B-InstructAi2 | 7.3B | 7.2 GBRuns well | ~13 tok/s | Comfortable |
| Qwen3-Next-80B-A3B-InstructAlibaba / Qwen · MoE | 81B | 52 GBRuns well | ~6.0 tok/s16B active | Slow |
| GLM-4.6V-FlashZhipu AI | 10B | 7.0 GBRuns well | ~9.5 tok/s | Comfortable |
| ERNIE-4.5-VL-28B-A3B-PTBaidu AI · MoE | 29B | 20 GBRuns well | ~3.3 tok/s | Slow |
| Olmo-3.1-32B-InstructAi2 | 32B | 23 GBRuns well | ~3.0 tok/s | Slow |
| ERNIE-4.5-21B-A3B-PTBaidu AI · MoE | 22B | 15 GBRuns well | ~4.3 tok/s | Slow |
| ERNIE-4.5-0.3B-PTBaidu AI | 361M | 853 MBRuns well | ~243 tok/s | Fast |
All figures at Q4_K_M quantization, 4K context. “Memory” includes the model, its context cache and runtime overhead. Speed is calculated, not benchmarked: 90 GB/s × 0.65 ÷ bytes read per token, assuming a DDR5-5600. Scale it by your own machine's bandwidth from the list above. Mixture-of-experts models read only their active parameters per token, which is why some large ones outrun smaller dense models.
Image generators that also fit
These make pictures rather than text. Their memory use scales with the resolution you generate at, not conversation length, so treat these figures as a floor rather than a ceiling.
What you'd need more memory for
The next models up, and what they ask for.