BENCHMARK REPORT
Can I Run Codestral 22B (Mistral AI) on NVIDIA RTX A5000 (24GB)?
Comprehensive technical verdict based on static weights, dynamic KV Cache (8k baseline context window), and CUDA runtime overhead.
Official Verdict
YES — Runs Smoothly
Executive Hardware Compatibility Summary: Codestral 22B (Mistral AI) on NVIDIA RTX A5000 (24GB)
Authoritative verdict, memory allocation, and token generation performance:
Direct Answer: Can Run PerfectlyCodestral 22B (Mistral AI) runs smoothly with 100% dedicated VRAM allocation on NVIDIA RTX A5000 (24GB) (24GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 17.02 GB (13.52 GB weights + 1.85 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical GPU memory provides 6.98 GB of free safety buffer, preventing PCIe memory bus throttling. Estimated generation throughput is ~39.8 tokens/sec (~25.1 ms per token) with prompt prefill processing reaching ~1050 tok/s.
Run Verdict
100% Fit (Optimal)
Total Required VRAM
17.02 GB / 24 GB
Decoding Speed
~39.8 tok/s (25.1ms)
Safe Context Window
Up to 32k tokens
Hardware Architecture & VRAM Topology
AmpereNVIDIA RTX A5000 (24GB) • 768 GB/s Bandwidth
Token Generation
~39.8 tok/s(25.1 ms)
OPTIMAL
VRAM Utilization Breakdown17.02 GB / 24 GB (71%)
13.52G
+6.98G Free
Weights
13.52 GB
KV Cache
1.85 GB
Activation
0.5 GB
OS/Driver
1.15 GB
Free Headroom
6.98 GB
Max Safe Context Limit on this Hardware
You can safely expand prompts up to 32,768 tokens before CUDA OOM.
Headroom Used
8,192 / 32,768
Context Window vs VRAM Scaling Curve
Blue line indicates total VRAM demand across context lengths; Red dashed line is physical VRAM ceiling.Max Safe: 32,768 tokens
Can Run Perfectly
optimalEntire model & KV cache fit fully into VRAM with pure GPU acceleration.
Throughput
~39.8 tok/s
25.1 ms / token
Headroom
6.98 GB
71% of capacity
Layers Fit
56 / 56
100% in VRAM
Max Safe Ctx
32k
Tokens before OOM
Static Permalink:
Open Static Report/can-i-run/codestral-22b-on-rtx-a5000Ready-to-Run CLI Commands
ollama run codestral:22bCommands include automatic memory ceiling flags and model layer offload configurations calculated above.
Cluster Scaled Serving
gpuy.dev On-Demand ComputeFine-Tune or Scale Codestral 22B (Mistral AI) on NVIDIA RTX 4090 (24GB)
Local inference runs well, but LoRA fine-tuning and multi-user vLLM serving require 40GB+ VRAM with FP8/FP16 optimizer states. Spin up dedicated clusters starting from $0.35/hr.
2x Faster Speed
$0.35/hr
Breakeven: ~5,286 hrs vs hardware purchase
Per-second billing
Launch Cloud Node →Pre-configured environment
Hardware Specification Summary
GPU ArchitectureAmpere
Memory Bandwidth768 GB/s
Model Parameters22.2B (LLM)
Attention ArchitectureGrouped-Query (GQA)
Prompt Prefill Speed~1050 tok/s (TTFT ~7802ms)
Benchmark Context Tested8,192 tokens
Global Compatibility Matrix for NVIDIA RTX A5000 (24GB)
Evaluated at Standard Q4_K_M Precision with 8,192 Tokens Context Window
Compatibility Index
75% (43/57)
39 Pass4 Tight14 OOM
| Model & Architecture | Category | Parameters | Required VRAM | Fit Status | Est. Speed | Simulate |
|---|---|---|---|---|---|---|
DeepSeek-R1 (671B Full MoE)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-387.16G) | ~0.7 tok/s | Simulate |
DeepSeek-V3 (671B Base/Chat)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-387.16G) | ~0.7 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-70B DeepSeek | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~2.6 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-32B DeepSeek | LLM | 32.8B | 23.75 GB | Tight Fit | ~26.9 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-14B DeepSeek | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~60.1 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-8B DeepSeek | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~110.3 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-8B DeepSeek | LLM | 8.03B | 7.48 GB | 100% VRAM Fit | ~109.9 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-7B DeepSeek | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~116.2 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-1.5B DeepSeek | LLM | 1.8B | 2.61 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-Coder-V2 Lite (16B / 2.4B MoE)MLAMoE DeepSeek | MoE | 15.7B(2.4B active) | 11.21 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-Coder-V2 (236B / 21B MoE)MLAMoE DeepSeek | MoE | 236B(21B active) | 146.23 GB | OOM (-122.23G) | ~2.9 tok/s | Simulate |
Qwen 2.5 Coder 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~2.5 tok/s | Simulate |
Qwen 2.5 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~2.5 tok/s | Simulate |
Qwen 2.5 VL 72B (Vision Flagship) Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~2.5 tok/s | Simulate |
Qwen 2.5 VL 7B (Vision) Qwen | LLM | 7.61B | 6.5 GB | 100% VRAM Fit | ~116 tok/s | Simulate |
Qwen 2.5 Coder 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | Tight Fit | ~26.9 tok/s | Simulate |
Qwen 2.5 Coder 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~60.1 tok/s | Simulate |
Qwen 2.5 Coder 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~116.2 tok/s | Simulate |
Qwen 2.5 Coder 1.5B Instruct Qwen | LLM | 1.54B | 2.45 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | Tight Fit | ~26.9 tok/s | Simulate |
Qwen 2.5 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~60.1 tok/s | Simulate |
Qwen 2.5 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~116.2 tok/s | Simulate |
Qwen 2.5 3B Instruct Qwen | LLM | 3.1B | 3.59 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 0.5B Instruct Qwen | LLM | 0.49B | 1.8 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.3 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~2.6 tok/s | Simulate |
Llama 3.1 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~2.6 tok/s | Simulate |
Llama 3.1 405B Instruct Llama | LLM | 405B | 252.77 GB | OOM (-228.77G) | ~0.6 tok/s | Simulate |
Llama 3.1 8B Instruct Llama | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~110.3 tok/s | Simulate |
Llama 3.2 3B Instruct Llama | LLM | 3.2B | 4.28 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.2 1B Instruct Llama | LLM | 1.23B | 2.41 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.2 11B Vision Instruct Llama | LLM | 10.6B | 8.92 GB | 100% VRAM Fit | ~83.3 tok/s | Simulate |
Llama 3.2 90B Vision Instruct Llama | LLM | 89B | 59.46 GB | OOM (-35.46G) | ~1.6 tok/s | Simulate |
Microsoft Phi-4 (14B) Phi | LLM | 14.7B | 12.01 GB | 100% VRAM Fit | ~60.1 tok/s | Simulate |
Microsoft Phi-3.5 Mini (3.8B) Phi | LLM | 3.82B | 6.91 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Google Gemma 2 9B Instruct Gemma | LLM | 9.2B | 9.79 GB | 100% VRAM Fit | ~96 tok/s | Simulate |
Mistral Small 3 (24B Instruct) Mistral | LLM | 24B | 18.12 GB | 100% VRAM Fit | ~36.8 tok/s | Simulate |
Mistral NeMo (12B Instruct) Mistral | LLM | 12.2B | 10.15 GB | 100% VRAM Fit | ~72.4 tok/s | Simulate |
Mistral 7B Instruct v0.3 Mistral | LLM | 7.25B | 6.88 GB | 100% VRAM Fit | ~121.8 tok/s | Simulate |
Codestral 22B (Mistral AI) Mistral | LLM | 22.2B | 17.02 GB | 100% VRAM Fit | ~39.8 tok/s | Simulate |
Mistral Large 2 (123B) Mistral | LLM | 123B | 79.77 GB | OOM (-55.77G) | ~0.9 tok/s | Simulate |
Mixtral 8x7B (MoE 47B)MoE Mistral | MoE | 46.7B(12.9B active) | 31.15 GB | OOM (-7.15G) | ~22.7 tok/s | Simulate |
Mixtral 8x22B (MoE 141B)MoE Mistral | MoE | 141B(39B active) | 89.67 GB | OOM (-65.67G) | ~2.6 tok/s | Simulate |
Pixtral 12B (Multimodal) Mistral | LLM | 12.4B | 10.28 GB | 100% VRAM Fit | ~71.2 tok/s | Simulate |
Gemma 2 27B Instruct Gemma | LLM | 27.2B | 21.26 GB | Tight Fit | ~32.5 tok/s | Simulate |
Google Gemma 2 2B Instruct Gemma | LLM | 2.6B | 3.84 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
GLM-4 9B Chat (Zhipu AI) GLM | LLM | 9.4B | 7.79 GB | 100% VRAM Fit | ~93.9 tok/s | Simulate |
FLUX.1 [dev] (12B DiT) FLUX | ImageGen | 12B | 15.68 GB | 100% VRAM Fit | ~73.6 tok/s | Simulate |
FLUX.1 [schnell] (4-step) FLUX | ImageGen | 12B | 15.68 GB | 100% VRAM Fit | ~73.6 tok/s | Simulate |
Stable Diffusion XL (SDXL 1.0) SDXL | ImageGen | 3.5B | 7.51 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Stable Diffusion 1.5 (Classic) SD | ImageGen | 1B | 5.78 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Whisper Large v3 (1.5B) Whisper | Audio | 1.5B | 3.64 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Kokoro 82M (Text-to-Speech) Kokoro | Audio | 0.082B | 1.56 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Wan2.1 (14B Text-to-Video) Wan | ImageGen | 14.2B | 18.72 GB | 100% VRAM Fit | ~62.2 tok/s | Simulate |
Wan2.1 (1.3B Video) Wan | ImageGen | 1.3B | 7.28 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
HunyuanVideo (13B Video DiT) Hunyuan | ImageGen | 13B | 19.69 GB | 100% VRAM Fit | ~67.9 tok/s | Simulate |
Stable Diffusion 3.5 Large (8B) SD | ImageGen | 8.1B | 13.3 GB | 100% VRAM Fit | ~109 tok/s | Simulate |
BAAI BGE-M3 (Multilingual Embedding) BAAI | LLM | 0.56B | 2.54 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Codestral 22B (Mistral AI) Context Scaling Matrix on NVIDIA RTX A5000 (24GB)
Dynamic KV Cache footprint and generation throughput measured across sequence lengths (Q4_K_M weights):
| Context Window | KV Cache Memory | Total VRAM Demand | Compatibility Status | Offloaded Layers | Est. Generation Speed |
|---|---|---|---|---|---|
| 2,048 tokens (2k) | 0.46 GB | 15.63 GB / 24 GB | 100% Fit | 0 (All in VRAM) | ~39.8 tok/s |
| 4,096 tokens (4k) | 0.93 GB | 16.10 GB / 24 GB | 100% Fit | 0 (All in VRAM) | ~39.8 tok/s |
| 8,192 tokens (8k)Standard Baseline | 1.85 GB | 17.02 GB / 24 GB | 100% Fit | 0 (All in VRAM) | ~39.8 tok/s |
| 16,384 tokens (16k) | 3.71 GB | 18.88 GB / 24 GB | 100% Fit | 0 (All in VRAM) | ~39.8 tok/s |
| 32,768 tokens (32k) | 7.42 GB | 22.59 GB / 24 GB | Tight Headroom | 0 (All in VRAM) | ~39.8 tok/s |
Hardware Compatibility Q&A: Codestral 22B (Mistral AI) on NVIDIA RTX A5000 (24GB)
Technical answers for memory allocation, CUDA overhead, and execution parameters.
Can NVIDIA RTX A5000 (24GB) run Codestral 22B (Mistral AI)?
Can Run Perfectly. Entire model & KV cache fit fully into VRAM with pure GPU acceleration. At an 8k context window, total VRAM demand is 17.02 GB compared to NVIDIA RTX A5000 (24GB)'s 24 GB frame buffer.
How much VRAM does Codestral 22B (Mistral AI) require?
At standard Q4_K_M quantization and 8k context, Codestral 22B (Mistral AI) requires approximately 17.02 GB of VRAM. This includes 13.52 GB for model weights, 1.85 GB for dynamic KV cache (Grouped-Query Attention (GQA)), 0.5 GB for activation buffers, and 1.15 GB for OS display and CUDA runtime overhead.
What generation speed (tokens/sec) can I expect on NVIDIA RTX A5000 (24GB)?
Estimated autoregressive token generation speed is ~39.8 tokens per second with a per-token latency of ~25.1 ms, powered by 768 GB/s memory bandwidth. Prompt prefill processing reaches ~1050 tok/s (TTFT ~7802 ms).
What causes CUDA Out-of-Memory (OOM) when running Codestral 22B (Mistral AI)?
CUDA OOM occurs when total memory consumption exceeds NVIDIA RTX A5000 (24GB)'s 24 GB physical VRAM. While model weights stay static, the KV cache grows with every conversation token. When VRAM fills up, inference engines (like Ollama or llama.cpp) must offload remaining layers across the PCIe bus (31.5 GB/s) to host system RAM, causing generation speed to drop sharply.
How do I run Codestral 22B (Mistral AI) locally on NVIDIA RTX A5000 (24GB)?
You can run Codestral 22B (Mistral AI) directly with Ollama using "ollama run codestral-22b". For advanced serving with vLLM or llama.cpp, copy the ready-to-run CLI commands in the CLI Commands section above with optimal context, quantization, and GPU layer offload parameters.
Related Hardware Benchmarks & Model Alternatives
Compare Codestral 22B (Mistral AI) and NVIDIA RTX A5000 (24GB) against alternative configurations:
Recommended Alternative GPUs for Codestral 22B (Mistral AI)
Explore how Codestral 22B (Mistral AI) performs across other budget, midrange, and flagship architectures:
Other Popular Models You Can Run on NVIDIA RTX A5000 (24GB)
Verified open-weights LLMs and image models that fit within NVIDIA RTX A5000 (24GB)'s 24GB VRAM:
Dual GPU Showdown: NVIDIA RTX A5000 (24GB) vs NVIDIA GeForce RTX 4090
Compare memory bandwidth (768 GB/s vs 1008 GB/s), VRAM headroom (24GB vs 24GB), and inference speed head-to-head.
Frequently Asked Questions (FAQ)
Model weights are only one part of total VRAM consumption. When you prompt a model, the KV Cache (Key-Value Cache) stores all historical tokens. At 32k or 64k context, standard GQA attention can demand an additional 4GB to 12GB of VRAM! Combined with activation buffers and OS background consumption (0.8GB on Windows), total memory spikes beyond hardware capacity.