HARDWARE FIT CHECK • Mistral Small 3 24B Instruct + NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)18.33 GB / 16 GB VRAM

Can I Run Mistral Small 3 24B Instruct on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Mistral Small 3 24B Instruct (24B) on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7) (16 GB).

! PARTIAL OFFLOAD18.33 GB (115%)~9 t/s

Mistral Small 3 24B Instruct on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)

! Partial Offload — Slow CPU/RAM Spillover

! PARTIAL OFFLOAD

Required VRAM

18.33 GB

115% of hardware capacity (16 GB)

Est. Decode Speed

~9 tok/s

Dense model @ 896 GB/s

Available Headroom

-2.33 GB

Needs +2.33 GB more VRAM

Memory Allocation & Headroom

18.33 GB allocated of 16 GB capacity

115% utilized

Weights: 14.4 GB

KV: (32K): 2.68 GB

Overhead: 1.25 GB

Deficit: -2.33 GB

Architecture & Throughput Transparency: Single-user decode speed (~9 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7) (896 GB/s). Prompt prefill / TTFT (~131 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

Partial Offload: Dedicated 100% GPU Options

High-bandwidth VRAM specs and current market prices

Consumer GPU✓ Fits Model

32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP

$1,999Amazon Prime →

Consumer GPU✓ Fits Model

24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP

$1,699Amazon Prime →

Multi-GPU Rig✓ Fits Model

48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP

$1,550Amazon Prime →

Consumer GPU

16 GB VRAM

NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)

Next-gen 16GB Blackwell GPU for fast 14B–32B Q4_K_M coding

896 GB/s300W TDP

$749Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

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Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	48 GB	2.68 GB	53.11 GB (332%)	0 t/s	OOM
Q8_08.5 bpw	25.44 GB	2.68 GB	29.76 GB (186%)	0 t/s	OOM
Q6_K6.56 bpw	19.68 GB	2.68 GB	23.8 GB (149%)	0 t/s	OOM
Q5_K_M5.68 bpw	17.04 GB	2.68 GB	21.07 GB (132%)	~8 t/s	PARTIAL
Q4_K_M4.8 bpw	14.4 GB	2.68 GB	18.33 GB (115%)	~9 t/s	PARTIAL
FP44.25 bpw	12.72 GB	2.68 GB	16.6 GB (104%)	~10 t/s	PARTIAL
IQ3_XS3.3 bpw	9.84 GB	2.68 GB	13.61 GB (85%)	~58 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Mistral Small 3 24B Instruct on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)

How much VRAM does Mistral Small 3 24B Instruct need on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Mistral Small 3 24B Instruct requires 18.33 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Mistral Small 3 24B Instruct achieve on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?

Based on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)'s 896 GB/s memory bandwidth, expected decode speed is approximately 9 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.