BENCHMARK REPORT
Can I Run Mistral NeMo (12B Instruct) on NVIDIA GeForce RTX 3060 (8GB)?
Comprehensive technical verdict based on static weights, dynamic KV Cache (8k baseline context window), and CUDA runtime overhead.
Official Verdict
NO — CUDA OOM
Executive Hardware Compatibility Summary: Mistral NeMo (12B Instruct) on NVIDIA GeForce RTX 3060 (8GB)
Authoritative verdict, memory allocation, and token generation performance:
Direct Answer: CUDA OOM — Deficit of 2.2 GBMistral NeMo (12B Instruct) exceeds dedicated physical VRAM and triggers PCIe layer offloading on NVIDIA GeForce RTX 3060 (8GB) (8GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 10.15 GB (7.43 GB weights + 1.33 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical VRAM is exceeded by 2.15 GB, forcing 12 transformer layers to offload across the PCIe bus to host DDR RAM. Estimated generation throughput is ~7.4 tokens/sec (~135.1 ms per token) with prompt prefill processing reaching ~775 tok/s.
Run Verdict
CUDA OOM (Offload)
Total Required VRAM
10.15 GB / 8 GB
Decoding Speed
~7.4 tok/s (135.1ms)
Safe Context Window
Offload to RAM
Hardware Architecture & VRAM Topology
AmpereNVIDIA GeForce RTX 3060 (8GB) • 240 GB/s Bandwidth
Token Generation
~7.4 tok/s(135.1 ms)
OOM SPILL
VRAM Utilization Breakdown10.15 GB / 8 GB (127%)
VRAM Limit (8G)
7.43G
1.33G
1.15G
-2.15G Spill
Weights
7.43 GB
KV Cache
1.33 GB
Activation
0.25 GB
OS/Driver
1.15 GB
Overflow
2.15 GB
Hardware Chokepoint: PCIe Bus Throttling Active
GPU VRAM (8 GB)
28 / 40 Layers Loaded
240 GB/s Bus
PCIe 4.0 x16 Bottleneck
31.5 GB/s
-97% Bandwidth Drop
Host DDR5 System RAM
12 Layers Offloaded
+2.6 GB RAM Siphoned
Why does your generation speed collapse to ~7.4 tok/s? Because for every single generated token, model weights across the 12 offloaded layers must be transferred across the 31.5 GB/s PCIe slot from host system RAM.
Context Window vs VRAM Scaling Curve
Blue line indicates total VRAM demand across context lengths; Red dashed line is physical VRAM ceiling.
CUDA OOM — Deficit of 2.2 GB
oom12/40 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 7.4 tok/s).
Throughput
~7.4 tok/s
135.1 ms / token
Headroom
-2.15 GB
127% of capacity
Layers Fit
28 / 40
12 on DDR5
Max Safe Ctx
0
Tokens before OOM
Host DDR RAM Status:Fits in DDR RAM (+2.6 GB used of 32 GB)
Static Permalink:
Open Static Report/can-i-run/mistral-nemo-12b-on-rtx-3060-8gReady-to-Run CLI Commands
# Note: Model requires CPU offloading (28/40 layers on GPU)
ollama run mistral-nemo:12bCommands include automatic memory ceiling flags and model layer offload configurations calculated above.
Cloud Hardware Failover
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Hardware Specification Summary
GPU ArchitectureAmpere
Memory Bandwidth240 GB/s
Model Parameters12.2B (LLM)
Attention ArchitectureGrouped-Query (GQA)
Prompt Prefill Speed~775 tok/s (TTFT ~10570ms)
Benchmark Context Tested8,192 tokens
Global Compatibility Matrix for NVIDIA GeForce RTX 3060 (8GB)
Evaluated at Standard Q4_K_M Precision with 8,192 Tokens Context Window
Compatibility Index
40% (23/57)
17 Pass6 Tight34 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 (-403.16G) | ~0.6 tok/s | Simulate |
DeepSeek-V3 (671B Base/Chat)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-403.16G) | ~0.6 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-70B DeepSeek | LLM | 70.6B | 47.6 GB | OOM (-39.6G) | ~0.6 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-32B DeepSeek | LLM | 32.8B | 23.75 GB | OOM (-15.75G) | ~1 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-14B DeepSeek | LLM | 14.7B | 11.94 GB | OOM (-3.94G) | ~4.9 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-8B DeepSeek | LLM | 8B | 7.33 GB | Tight Fit | ~34.5 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-8B DeepSeek | LLM | 8.03B | 7.48 GB | Tight Fit | ~34.4 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-7B DeepSeek | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~36.3 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-1.5B DeepSeek | LLM | 1.8B | 2.61 GB | 100% VRAM Fit | ~153.3 tok/s | Simulate |
DeepSeek-Coder-V2 Lite (16B / 2.4B MoE)MLAMoE DeepSeek | MoE | 15.7B(2.4B active) | 11.21 GB | OOM (-3.21G) | ~34.4 tok/s | Simulate |
DeepSeek-Coder-V2 (236B / 21B MoE)MLAMoE DeepSeek | MoE | 236B(21B active) | 146.23 GB | OOM (-138.23G) | ~0.6 tok/s | Simulate |
Qwen 2.5 Coder 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-40.87G) | ~0.6 tok/s | Simulate |
Qwen 2.5 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-40.87G) | ~0.6 tok/s | Simulate |
Qwen 2.5 VL 72B (Vision Flagship) Qwen | LLM | 72.7B | 48.87 GB | OOM (-40.87G) | ~0.6 tok/s | Simulate |
Qwen 2.5 VL 7B (Vision) Qwen | LLM | 7.61B | 6.5 GB | 100% VRAM Fit | ~36.2 tok/s | Simulate |
Qwen 2.5 Coder 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | OOM (-15.75G) | ~1 tok/s | Simulate |
Qwen 2.5 Coder 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | OOM (-3.94G) | ~4.9 tok/s | Simulate |
Qwen 2.5 Coder 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~36.3 tok/s | Simulate |
Qwen 2.5 Coder 1.5B Instruct Qwen | LLM | 1.54B | 2.45 GB | 100% VRAM Fit | ~179.1 tok/s | Simulate |
Qwen 2.5 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | OOM (-15.75G) | ~1 tok/s | Simulate |
Qwen 2.5 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | OOM (-3.94G) | ~4.9 tok/s | Simulate |
Qwen 2.5 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~36.3 tok/s | Simulate |
Qwen 2.5 3B Instruct Qwen | LLM | 3.1B | 3.59 GB | 100% VRAM Fit | ~89 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 (-39.6G) | ~0.6 tok/s | Simulate |
Llama 3.1 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-39.6G) | ~0.6 tok/s | Simulate |
Llama 3.1 405B Instruct Llama | LLM | 405B | 252.77 GB | OOM (-244.77G) | ~0.6 tok/s | Simulate |
Llama 3.1 8B Instruct Llama | LLM | 8B | 7.33 GB | Tight Fit | ~34.5 tok/s | Simulate |
Llama 3.2 3B Instruct Llama | LLM | 3.2B | 4.28 GB | 100% VRAM Fit | ~86.2 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 | OOM (-0.92G) | ~10.2 tok/s | Simulate |
Llama 3.2 90B Vision Instruct Llama | LLM | 89B | 59.46 GB | OOM (-51.46G) | ~0.6 tok/s | Simulate |
Microsoft Phi-4 (14B) Phi | LLM | 14.7B | 12.01 GB | OOM (-4.01G) | ~5 tok/s | Simulate |
Microsoft Phi-3.5 Mini (3.8B) Phi | LLM | 3.82B | 6.91 GB | 100% VRAM Fit | ~72.2 tok/s | Simulate |
Google Gemma 2 9B Instruct Gemma | LLM | 9.2B | 9.79 GB | OOM (-1.79G) | ~9.4 tok/s | Simulate |
Mistral Small 3 (24B Instruct) Mistral | LLM | 24B | 18.12 GB | OOM (-10.12G) | ~1.8 tok/s | Simulate |
Mistral NeMo (12B Instruct) Mistral | LLM | 12.2B | 10.15 GB | OOM (-2.15G) | ~7.4 tok/s | Simulate |
Mistral 7B Instruct v0.3 Mistral | LLM | 7.25B | 6.88 GB | 100% VRAM Fit | ~38 tok/s | Simulate |
Codestral 22B (Mistral AI) Mistral | LLM | 22.2B | 17.02 GB | OOM (-9.02G) | ~2.1 tok/s | Simulate |
Mistral Large 2 (123B) Mistral | LLM | 123B | 79.77 GB | OOM (-71.77G) | ~0.6 tok/s | Simulate |
Mixtral 8x7B (MoE 47B)MoE Mistral | MoE | 46.7B(12.9B active) | 31.15 GB | OOM (-23.15G) | ~2.1 tok/s | Simulate |
Mixtral 8x22B (MoE 141B)MoE Mistral | MoE | 141B(39B active) | 89.67 GB | OOM (-81.67G) | ~0.6 tok/s | Simulate |
Pixtral 12B (Multimodal) Mistral | LLM | 12.4B | 10.28 GB | OOM (-2.28G) | ~7.1 tok/s | Simulate |
Gemma 2 27B Instruct Gemma | LLM | 27.2B | 21.26 GB | OOM (-13.26G) | ~1.2 tok/s | Simulate |
Google Gemma 2 2B Instruct Gemma | LLM | 2.6B | 3.84 GB | 100% VRAM Fit | ~106.1 tok/s | Simulate |
GLM-4 9B Chat (Zhipu AI) GLM | LLM | 9.4B | 7.79 GB | Tight Fit | ~29.3 tok/s | Simulate |
FLUX.1 [dev] (12B DiT) FLUX | ImageGen | 12B | 15.68 GB | OOM (-7.68G) | ~3.3 tok/s | Simulate |
FLUX.1 [schnell] (4-step) FLUX | ImageGen | 12B | 15.68 GB | OOM (-7.68G) | ~3.3 tok/s | Simulate |
Stable Diffusion XL (SDXL 1.0) SDXL | ImageGen | 3.5B | 7.51 GB | Tight Fit | ~78.8 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 | OOM (-10.72G) | ~2.2 tok/s | Simulate |
Wan2.1 (1.3B Video) Wan | ImageGen | 1.3B | 7.28 GB | Tight Fit | ~180 tok/s | Simulate |
HunyuanVideo (13B Video DiT) Hunyuan | ImageGen | 13B | 19.69 GB | OOM (-11.69G) | ~2.3 tok/s | Simulate |
Stable Diffusion 3.5 Large (8B) SD | ImageGen | 8.1B | 13.3 GB | OOM (-5.3G) | ~6 tok/s | Simulate |
BAAI BGE-M3 (Multilingual Embedding) BAAI | LLM | 0.56B | 2.54 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Mistral NeMo (12B Instruct) Context Scaling Matrix on NVIDIA GeForce RTX 3060 (8GB)
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) | 0.33 GB | 9.16 GB / 8 GB | CUDA OOM | 7 layers on RAM | ~8.6 tok/s |
| 4,096 tokens (4k) | 0.66 GB | 9.49 GB / 8 GB | CUDA OOM | 9 layers on RAM | ~8.2 tok/s |
| 8,192 tokens (8k)Standard Baseline | 1.33 GB | 10.15 GB / 8 GB | CUDA OOM | 12 layers on RAM | ~7.4 tok/s |
| 16,384 tokens (16k) | 2.65 GB | 11.48 GB / 8 GB | CUDA OOM | 19 layers on RAM | ~5.7 tok/s |
| 32,768 tokens (32k) | 5.30 GB | 14.13 GB / 8 GB | CUDA OOM | 34 layers on RAM | ~2.1 tok/s |
Hardware Compatibility Q&A: Mistral NeMo (12B Instruct) on NVIDIA GeForce RTX 3060 (8GB)
Technical answers for memory allocation, CUDA overhead, and execution parameters.
Can NVIDIA GeForce RTX 3060 (8GB) run Mistral NeMo (12B Instruct)?
CUDA OOM — Deficit of 2.2 GB. 12/40 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 7.4 tok/s). At an 8k context window, total VRAM demand is 10.15 GB compared to NVIDIA GeForce RTX 3060 (8GB)'s 8 GB frame buffer.
How much VRAM does Mistral NeMo (12B Instruct) require?
At standard Q4_K_M quantization and 8k context, Mistral NeMo (12B Instruct) requires approximately 10.15 GB of VRAM. This includes 7.43 GB for model weights, 1.33 GB for dynamic KV cache (Grouped-Query Attention (GQA)), 0.25 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 GeForce RTX 3060 (8GB)?
Estimated autoregressive token generation speed is ~7.4 tokens per second with a per-token latency of ~135.1 ms, powered by 240 GB/s memory bandwidth. Prompt prefill processing reaches ~775 tok/s (TTFT ~10570 ms).
What causes CUDA Out-of-Memory (OOM) when running Mistral NeMo (12B Instruct)?
CUDA OOM occurs when total memory consumption exceeds NVIDIA GeForce RTX 3060 (8GB)'s 8 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 Mistral NeMo (12B Instruct) locally on NVIDIA GeForce RTX 3060 (8GB)?
You can run Mistral NeMo (12B Instruct) directly with Ollama using "ollama run mistral-nemo-12b". 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 Mistral NeMo (12B Instruct) and NVIDIA GeForce RTX 3060 (8GB) against alternative configurations:
Recommended Alternative GPUs for Mistral NeMo (12B Instruct)
Explore how Mistral NeMo (12B Instruct) performs across other budget, midrange, and flagship architectures:
Other Popular Models You Can Run on NVIDIA GeForce RTX 3060 (8GB)
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Dual GPU Showdown: NVIDIA GeForce RTX 3060 (8GB) vs NVIDIA GeForce RTX 4090
Compare memory bandwidth (240 GB/s vs 1008 GB/s), VRAM headroom (8GB 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.