Can I Run InternLM 3 on NVIDIA GeForce RTX 3090?
Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, comfortably — InternLM 3 20B Instruct at Q4_K_M needs 14.5 GB at 8K context (12.1 GB weights + 2.4 GB KV cache/overhead), leaving ~9.5 GB of the NVIDIA GeForce RTX 3090's 24 GB, at ~41 tok/s (est.), with room for up to 32K context.
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NVIDIA GeForce RTX 3090 Specs
| VRAM | 24 GB |
| Memory Bandwidth | 936 GB/s |
InternLM 3 20B Instruct on the NVIDIA GeForce RTX 3090: VRAM by quantization
| Quant | VRAM needed | Fits 24 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 42.4 GB | ✗ No | — | — |
| Q8_0 | 23.6 GB | ✓ Yes | 8K | Build for this |
| Q6_K | 18.8 GB | ✓ Yes | 32K | — |
| Q5_K_M | 16.6 GB | ✓ Yes | 32K | — |
| Q4_K_M | 14.5 GB | ✓ Yes | 32K | Build for this |
| Q3_K_M | 10.9 GB | ✓ Yes | 32K | — |
| Q2_K | 9 GB | ✓ Yes | 32K | — |
Assumes an 8K-token context with an f16 KV cache, on an estimated architecture — this model publishes no config we can read. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 24 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 3090: 8K 14.5 GB · 32K 19.2 GB. Every window this model publishes still fits 24 GB.
No compatible GPU? InternLM 3 20B Instruct on 24 GB of system RAM, CPU only: runs.
InternLM 3 Sizes That Fit the NVIDIA GeForce RTX 3090 (at 8K context)
| InternLM 3 20B Instruct | Q4_K_M · 14.5 GB at 8K context · ~41 tok/s (est.) |
| InternLM 3 8B Instruct | Q4_K_M · 6.5 GB at 8K context · ~85 tok/s (est.) |
At 2 hrs/day, buying (~$1,499) beats renting at $0.34/hr after about 6.1 years.
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FAQ
InternLM 3 on the NVIDIA GeForce RTX 3090 — does it fit?
Yes, comfortably — InternLM 3 20B Instruct at Q4_K_M needs 14.5 GB at 8K context (12.1 GB weights + 2.4 GB KV cache/overhead), leaving ~9.5 GB of the NVIDIA GeForce RTX 3090's 24 GB, at ~41 tok/s (est.), with room for up to 32K context.
Which InternLM 3 variant fits best on the NVIDIA GeForce RTX 3090?
InternLM 3 20B Instruct at Q4_K_M quantization needs 14.5 GB at 8K context (12.1 GB weights + 2.4 GB KV cache/overhead), estimated ~41 tokens/sec, up to 32K context.
Every InternLM 3 size on the NVIDIA GeForce RTX 3090
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| InternLM 3 20B Instruct | 14.5 GB | Runs | ~41 tok/s |
| InternLM 3 8B Instruct | 6.5 GB | Runs | ~85 tok/s |
InternLM 3 on Other GPUs
- InternLM 3 on NVIDIA GeForce RTX 4090
- InternLM 3 on AMD Radeon RX 7900 XTX
- InternLM 3 on Apple M4 Pro
- InternLM 3 on Apple M3
- InternLM 3 on Apple M2
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