Can I Run Magistral Small on NVIDIA GeForce RTX 5090?
Written by Jakub Rusinowski · Last updated September 29, 2026
Yes, comfortably — Magistral Small 24B at Q4_K_M needs 17 GB at 8K context (14.5 GB weights + 2.5 GB KV cache/overhead), leaving ~15 GB of the NVIDIA GeForce RTX 5090's 32 GB, at ~81 tok/s (est.), with room for up to 64K context.
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NVIDIA GeForce RTX 5090 Specs
| VRAM | 32 GB |
| Memory Bandwidth | 1792 GB/s |
Magistral Small 24B on the NVIDIA GeForce RTX 5090: VRAM by quantization
| Quant | VRAM needed | Fits 32 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 50.5 GB | ✗ No | — | — |
| Q8_0 | 28 GB | ✓ Yes | 16K | — |
| Q6_K | 22.2 GB | ✓ Yes | 32K | — |
| Q5_K_M | 19.5 GB | ✓ Yes | 64K | — |
| Q4_K_M | 17 GB | ✓ Yes | 64K | — |
| Q3_K_M | 12.7 GB | ✓ Yes | 64K | — |
| Q2_K | 10.4 GB | ✓ Yes | 64K | — |
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 32 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 5090: 8K 17 GB · 32K 22 GB · 128K 42.1 GB ✗. Past 128K it no longer fits 32 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Magistral Small 24B on 16 GB of system RAM, CPU only: runs, but it is tight.
Magistral Small Sizes That Fit the NVIDIA GeForce RTX 5090 (at 8K context)
| Magistral Small 24B | Q4_K_M · 17 GB at 8K context · ~81 tok/s (est.) |
At 2 hrs/day, buying (~$1,999) beats renting at $0.34/hr after about 8.2 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
Is the NVIDIA GeForce RTX 5090 enough to run Magistral Small locally?
Yes, comfortably — Magistral Small 24B at Q4_K_M needs 17 GB at 8K context (14.5 GB weights + 2.5 GB KV cache/overhead), leaving ~15 GB of the NVIDIA GeForce RTX 5090's 32 GB, at ~81 tok/s (est.), with room for up to 64K context.
What's the best Magistral Small size for the NVIDIA GeForce RTX 5090?
Magistral Small 24B at Q4_K_M quantization needs 17 GB at 8K context (14.5 GB weights + 2.5 GB KV cache/overhead), estimated ~81 tokens/sec, up to 64K context.
Magistral Small on Other GPUs
- Magistral Small on Apple M4
- Magistral Small on Apple M2 Pro
- Magistral Small on Apple M1 Pro
- Magistral Small on Apple M5
- Magistral Small on Apple M3 Pro
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