Can I Run InternLM 3 on 256 GB system RAM?
Written by Jakub Rusinowski · Last updated January 15, 2025
Yes — comfortably
Yes, comfortably — InternLM 3 20B Instruct at Q4_K_M needs about 14.5 GB of the 204.8 GB usable on 256 GB system RAM, leaving ~190.3 GB spare and running at ~5.2 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~5.2 tok/s
256 GB system RAM — what it gives a model
| Usable memory for models | 204.8 GB |
| Memory bandwidth | 90 GB/s |
InternLM 3 on 256 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 204.8 GB? | Max context | Est. speed | Download |
|---|
| F16 | 42.4 GB | ✓ Yes | 32K | ~1.6 tok/s | 40 GB |
| Q8_0 | 23.6 GB | ✓ Yes | 32K | ~3 tok/s | 21.3 GB |
| Q6_K | 18.8 GB | ✓ Yes | 32K | ~3.9 tok/s | 16.4 GB |
| Q5_K_M | 16.6 GB | ✓ Yes | 32K | ~4.4 tok/s | 14.2 GB |
| Q4_K_M | 14.5 GB | ✓ Yes | 32K | ~5.2 tok/s | 12.1 GB |
| Q3_K_M | 10.9 GB | ✓ Yes | 32K | ~7.1 tok/s | 8.5 GB |
| Q2_K | 9 GB | ✓ Yes | 32K | ~8.9 tok/s | 6.6 GB |
Which InternLM 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| InternLM 3 20B Instruct | 14.5 GB | ✓ Fits | ~5.2 tok/s |
| InternLM 3 8B Instruct | 6.5 GB | ✓ Fits | ~11.9 tok/s |
What to watch out for
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 204.8 GB of the 256 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run InternLM 3 on 256 GB system RAM?
Yes, comfortably — InternLM 3 20B Instruct at Q4_K_M needs about 14.5 GB of the 204.8 GB usable on 256 GB system RAM, leaving ~190.3 GB spare and running at ~5.2 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of InternLM 3 should I use on 256 GB system RAM?
Q4_K_M — it needs about 14.5 GB of the 204.8 GB available, downloads as roughly 12.1 GB, and runs at an estimated 5.2 tokens/sec with up to 32K of context.
What limits InternLM 3 on 256 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 256 GB system RAM
InternLM 3 on GPUs
What This Model Is Good At
Model & Tools
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