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HARDWARE FIT CHECK • Mistral Small 3 24B Instruct + NVIDIA GeForce RTX 5080 (16GB GDDR7)18.33 GB / 16 GB VRAM

Can I Run Mistral Small 3 24B Instruct on NVIDIA GeForce RTX 5080 (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 5080 (16GB GDDR7) (16 GB).

! PARTIAL OFFLOAD18.33 GB (115%)~10 t/s

Target Hardware & Model

interactive
High-throughput Apache 2.0 dense model matching 70B performance, ideal for single 24GB GPUs (RTX 4090/3090/5090) at FP8/Q8
Mistral Small 3 24B Instruct on NVIDIA GeForce RTX 5080 (16GB GDDR7)

! Partial Offload — Slow CPU/RAM Spillover

! PARTIAL OFFLOAD
Required VRAM
18.33 GB
115% of hardware capacity (16 GB)
Est. Decode Speed
~10 tok/s
Dense model @ 960 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 (~10 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5080 (16GB GDDR7) (960 GB/s). Prompt prefill / TTFT (~145 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 5080 (16GB GDDR7)

Blazing fast 8B–14B coding models with FP4/Q6_K precision

960 GB/s360W TDP
$999Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Mistral Small 3 24B Instruct @ 18.33 GB VRAM)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)
Launch 1-Click GPU Pod →
Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)
Rent Spot GPU →
Lambda GPU CloudEnterprise 8x H100 Clusters
$2.49/hr (H100) – $19.92/hr (8x H100)
Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw48 GB2.68 GB53.11 GB (332%)0 t/sOOM
Q8_08.5 bpw25.44 GB2.68 GB29.76 GB (186%)0 t/sOOM
Q6_K6.56 bpw19.68 GB2.68 GB23.8 GB (149%)0 t/sOOM
Q5_K_M5.68 bpw17.04 GB2.68 GB21.07 GB (132%)~8 t/sPARTIAL
Q4_K_M4.8 bpw14.4 GB2.68 GB18.33 GB (115%)~10 t/sPARTIAL
FP44.25 bpw12.72 GB2.68 GB16.6 GB (104%)~11 t/sPARTIAL
IQ3_XS3.3 bpw9.84 GB2.68 GB13.61 GB (85%)~62 t/sFITS
FAQ & Reference

Frequently Asked Questions: Mistral Small 3 24B Instruct on NVIDIA GeForce RTX 5080 (16GB GDDR7)

How much VRAM does Mistral Small 3 24B Instruct need on NVIDIA GeForce RTX 5080 (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 5080 (16GB GDDR7)?

Based on NVIDIA GeForce RTX 5080 (16GB GDDR7)'s 960 GB/s memory bandwidth, expected decode speed is approximately 10 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.