Home / Models / Magistral Small / NVIDIA RTX 5060 Ti 8 GB

Calculated memory fit · performance is a separate section

Can Magistral Small run on NVIDIA RTX 5060 Ti 8 GB?

Can it run? Yes, with CPU/RAM offload. Full GPU fit? No.

Can it run?

Can it run?
Yes, with CPU/RAM offload. This is not equivalent to full-GPU inference.
Full GPU fit?
No
Model
Magistral Small · 24B
GPU
NVIDIA RTX 5060 Ti 8 GB · 8 GB advertised

What else can NVIDIA RTX 5060 Ti 8 GB run? · Find other GPUs for Magistral Small · See 2× multi-GPU options

Memory calculation

Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.

Model weights13.8 GB
KV cache1.3 GB
Runtime reserve1.2 GB
Safety margin0.7 GB
Required (estimated)16.9 GB
GPU usable VRAM7.2 GB

Result: CPU RAM OFFLOAD REQUIRED

Quantization table

Quantization2K4K8K16K32K40K
BF16NoNoNoNoNoNo
FP16NoNoNoNoNoNo
Q3OffloadOffloadOffloadOffloadOffloadOffload
Q4OffloadOffloadOffloadOffloadOffloadOffload
Q5OffloadOffloadOffloadOffloadOffloadOffload
Q6NoNoNoNoNoNo
Q8NoNoNoNoNoNo

What fits entirely in VRAM?

No calculated full-VRAM fit at the evaluated quantizations and context lengths.

What requires RAM offload?

Weights may run with CPU/RAM offload. That is not equivalent to full-GPU inference. Performance numbers below are only published for full-VRAM fits.

Performance

No llama.cpp performance result is published for this pair yet. Memory fit above is unchanged. How performance is calculated

Available graphics cards

These SKUs use this GPU chip. Amazon CTAs appear only for EXACT/HIGH matches. Absence of a price does not change the compatibility result above.

Check price on Amazon

Sources

Model: official config mistralai/Magistral-Small-2506 (main). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: DOES_NOT_FIT. Amazon: affiliate commerce match, not a spec source.