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HARDWARE FIT CHECK • Mistral Large 2 (123B Instruct) + NVIDIA GeForce RTX 5080 (16GB GDDR7)83.04 GB / 16 GB VRAM

Can I Run Mistral Large 2 (123B 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 Large 2 (123B Instruct) (123B) on NVIDIA GeForce RTX 5080 (16GB GDDR7) (16 GB).

✕ OOM83.04 GB (519%)0 t/s

Model & Target Hardware

interactive
High-Precision Reasoning & Multilingual Enterprise Agent Workflows
GPU Topology / Quantity:Single GPU / Unified SoC
Mistral Large 2 (123B Instruct) on NVIDIA GeForce RTX 5080 (16GB GDDR7)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
83.04 GB
519% of hardware capacity (16 GB)
Est. Decode Speed
0 tok/s
Dense model @ 960 GB/s
Available Headroom
-67.04 GB
Needs +67.04 GB more VRAM
Memory Allocation & Headroom
83.04 GB allocated of 16 GB capacity
519% utilized
Weights: 73.8 GB
KV: (32K): 5.91 GB
Overhead: 3.33 GB
Deficit: -67.04 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5080 (16GB GDDR7) (960 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run mistral-large:123b

Local Hardware Configurations & Pricing

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High-bandwidth VRAM specs and current market prices
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48 GB VRAM

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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
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Apple Silicon✓ Fits Model
112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP
$3,499Amazon 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 Large 2 (123B Instruct) @ 83.04 GB VRAM)

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$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)
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Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw246 GB5.91 GB261.27 GB (1633%)0 t/sOOM
Q8_08.5 bpw130.38 GB5.91 GB141.6 GB (885%)0 t/sOOM
Q6_K6.56 bpw100.86 GB5.91 GB111.05 GB (694%)0 t/sOOM
Q5_K_M5.68 bpw87.33 GB5.91 GB97.05 GB (607%)0 t/sOOM
Q4_K_M4.8 bpw73.8 GB5.91 GB83.04 GB (519%)0 t/sOOM
FP44.25 bpw65.19 GB5.91 GB74.13 GB (463%)0 t/sOOM
IQ3_XS3.3 bpw50.43 GB5.91 GB58.86 GB (368%)0 t/sOOM

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FAQ & Reference

Frequently Asked Questions: Mistral Large 2 (123B Instruct) on NVIDIA GeForce RTX 5080 (16GB GDDR7)

How much VRAM does Mistral Large 2 (123B Instruct) need on NVIDIA GeForce RTX 5080 (16GB GDDR7)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Mistral Large 2 (123B Instruct) requires 83.04 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Mistral Large 2 (123B 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 0 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.