BENCHMARK REPORT

Can I Run Mixtral 8x7B (MoE 47B) on NVIDIA GeForce RTX 3070?

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: Mixtral 8x7B (MoE 47B) on NVIDIA GeForce RTX 3070

Authoritative verdict, memory allocation, and token generation performance:

Direct Answer: CUDA OOM — Deficit of 23.2 GBMixtral 8x7B (MoE 47B) exceeds dedicated physical VRAM and triggers PCIe layer offloading on NVIDIA GeForce RTX 3070 (8GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 31.15 GB (28.44 GB weights + 1.06 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical VRAM is exceeded by 23.15 GB, forcing 27 transformer layers to offload across the PCIe bus to host DDR RAM. Estimated generation throughput is ~3.4 tokens/sec (~294.1 ms per token) with prompt prefill processing reaching ~1319 tok/s.

Run Verdict

CUDA OOM (Offload)

Total Required VRAM

31.15 GB / 8 GB

Decoding Speed

~3.4 tok/s (294.1ms)

Safe Context Window

Offload to RAM

Hardware Architecture & VRAM Topology

Ampere

NVIDIA GeForce RTX 3070 • 448 GB/s Bandwidth

Token Generation

~3.4 tok/s(294.1 ms)

OOM SPILL

VRAM Utilization Breakdown31.15 GB / 8 GB (250%)

VRAM Limit (8G)

28.44G

-23.15G Spill

Weights

28.44 GB

KV Cache

1.06 GB

Activation

0.5 GB

OS/Driver

1.15 GB

Overflow

23.15 GB

Hardware Chokepoint: PCIe Bus Throttling Active

GPU VRAM (8 GB)

5 / 32 Layers Loaded

448 GB/s Bus

PCIe 4.0 x16 Bottleneck

31.5 GB/s

-97% Bandwidth Drop

Host DDR5 System RAM

27 Layers Offloaded

+27.6 GB RAM Siphoned

Why does your generation speed collapse to ~3.4 tok/s? Because for every single generated token, model weights across the 27 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 23.2 GB

oom

27/32 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 3.4 tok/s).

Throughput

~3.4 tok/s

294.1 ms / token

Headroom

-23.15 GB

250% of capacity

Layers Fit

5 / 32

27 on DDR5

Max Safe Ctx

Tokens before OOM

Host DDR RAM Status:EXCEEDS SYSTEM RAM (27.6 GB required > 32 GB RAM)!

Static Permalink:/can-i-run/mixtral-8x7b-instruct-on-rtx-3070

Open Static Report

Ready-to-Run CLI Commands

# Note: Model requires CPU offloading (5/32 layers on GPU)
ollama run mixtral:8x7b

Commands include automatic memory ceiling flags and model layer offload configurations calculated above.

Cloud Hardware Failover

gpuy.dev On-Demand Compute

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Launch Cloud Node →Pre-configured environment

Hardware Specification Summary

GPU ArchitectureAmpere

Memory Bandwidth448 GB/s

Model Parameters46.7B (MoE)

Attention ArchitectureGrouped-Query (GQA)

Prompt Prefill Speed~1319 tok/s (TTFT ~6211ms)

Benchmark Context Tested8,192 tokens

Customize in Full Matcher Simulator →

Global Compatibility Matrix for NVIDIA GeForce RTX 3070

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)	~1.7 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-14B DeepSeek	LLM	14.7B	11.94 GB	OOM (-3.94G)	~8.9 tok/s	Simulate
DeepSeek-R1-Distill-Llama-8B DeepSeek	LLM	8B	7.33 GB	Tight Fit	~64.4 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-8B DeepSeek	LLM	8.03B	7.48 GB	Tight Fit	~64.1 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-7B DeepSeek	LLM	7.6B	6.49 GB	100% VRAM Fit	~67.8 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	OOM (-3.21G)	~63 tok/s	Simulate
DeepSeek-Coder-V2 (236B / 21B MoE)MLAMoE DeepSeek	MoE	236B(21B active)	146.23 GB	OOM (-138.23G)	~0.8 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	~67.7 tok/s	Simulate
Qwen 2.5 Coder 32B Instruct Qwen	LLM	32.8B	23.75 GB	OOM (-15.75G)	~1.7 tok/s	Simulate
Qwen 2.5 Coder 14B Instruct Qwen	LLM	14.7B	11.94 GB	OOM (-3.94G)	~8.9 tok/s	Simulate
Qwen 2.5 Coder 7B Instruct Qwen	LLM	7.6B	6.49 GB	100% VRAM Fit	~67.8 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	OOM (-15.75G)	~1.7 tok/s	Simulate
Qwen 2.5 14B Instruct Qwen	LLM	14.7B	11.94 GB	OOM (-3.94G)	~8.9 tok/s	Simulate
Qwen 2.5 7B Instruct Qwen	LLM	7.6B	6.49 GB	100% VRAM Fit	~67.8 tok/s	Simulate
Qwen 2.5 3B Instruct Qwen	LLM	3.1B	3.59 GB	100% VRAM Fit	~166.1 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	~64.4 tok/s	Simulate
Llama 3.2 3B Instruct Llama	LLM	3.2B	4.28 GB	100% VRAM Fit	~160.9 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)	~18.9 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)	~9.1 tok/s	Simulate
Microsoft Phi-3.5 Mini (3.8B) Phi	LLM	3.82B	6.91 GB	100% VRAM Fit	~134.8 tok/s	Simulate
Google Gemma 2 9B Instruct Gemma	LLM	9.2B	9.79 GB	OOM (-1.79G)	~17.4 tok/s	Simulate
Mistral Small 3 (24B Instruct) Mistral	LLM	24B	18.12 GB	OOM (-10.12G)	~3.2 tok/s	Simulate
Mistral NeMo (12B Instruct) Mistral	LLM	12.2B	10.15 GB	OOM (-2.15G)	~13.7 tok/s	Simulate
Mistral 7B Instruct v0.3 Mistral	LLM	7.25B	6.88 GB	100% VRAM Fit	~71 tok/s	Simulate
Codestral 22B (Mistral AI) Mistral	LLM	22.2B	17.02 GB	OOM (-9.02G)	~3.7 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)	~3.4 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)	~13 tok/s	Simulate
Gemma 2 27B Instruct Gemma	LLM	27.2B	21.26 GB	OOM (-13.26G)	~1.9 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	Tight Fit	~54.8 tok/s	Simulate
FLUX.1 [dev] (12B DiT) FLUX	ImageGen	12B	15.68 GB	OOM (-7.68G)	~5.7 tok/s	Simulate
FLUX.1 [schnell] (4-step) FLUX	ImageGen	12B	15.68 GB	OOM (-7.68G)	~5.7 tok/s	Simulate
Stable Diffusion XL (SDXL 1.0) SDXL	ImageGen	3.5B	7.51 GB	Tight Fit	~147.1 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)	~3.8 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)	~3.9 tok/s	Simulate
Stable Diffusion 3.5 Large (8B) SD	ImageGen	8.1B	13.3 GB	OOM (-5.3G)	~10.6 tok/s	Simulate
BAAI BGE-M3 (Multilingual Embedding) BAAI	LLM	0.56B	2.54 GB	100% VRAM Fit	~180 tok/s	Simulate

Mixtral 8x7B (MoE 47B) Context Scaling Matrix on NVIDIA GeForce RTX 3070

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)	0.27 GB	30.36 GB / 8 GB	CUDA OOM	26 layers on RAM	~3.9 tok/s
4,096 tokens (4k)	0.53 GB	30.62 GB / 8 GB	CUDA OOM	26 layers on RAM	~3.9 tok/s
8,192 tokens (8k)Standard Baseline	1.06 GB	31.15 GB / 8 GB	CUDA OOM	27 layers on RAM	~3.4 tok/s
16,384 tokens (16k)	2.12 GB	32.21 GB / 8 GB	CUDA OOM	28 layers on RAM	~2.8 tok/s
32,768 tokens (32k)	4.24 GB	34.33 GB / 8 GB	CUDA OOM	30 layers on RAM	~1.7 tok/s

Hardware Compatibility Q&A: Mixtral 8x7B (MoE 47B) on NVIDIA GeForce RTX 3070

Technical answers for memory allocation, CUDA overhead, and execution parameters.

Can NVIDIA GeForce RTX 3070 run Mixtral 8x7B (MoE 47B)?

CUDA OOM — Deficit of 23.2 GB. 27/32 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 3.4 tok/s). At an 8k context window, total VRAM demand is 31.15 GB compared to NVIDIA GeForce RTX 3070's 8 GB frame buffer.

How much VRAM does Mixtral 8x7B (MoE 47B) require?

At standard Q4_K_M quantization and 8k context, Mixtral 8x7B (MoE 47B) requires approximately 31.15 GB of VRAM. This includes 28.44 GB for model weights, 1.06 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 GeForce RTX 3070?

Estimated autoregressive token generation speed is ~3.4 tokens per second with a per-token latency of ~294.1 ms, powered by 448 GB/s memory bandwidth. Prompt prefill processing reaches ~1319 tok/s (TTFT ~6211 ms).

What causes CUDA Out-of-Memory (OOM) when running Mixtral 8x7B (MoE 47B)?

CUDA OOM occurs when total memory consumption exceeds NVIDIA GeForce RTX 3070'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 Mixtral 8x7B (MoE 47B) locally on NVIDIA GeForce RTX 3070?

You can run Mixtral 8x7B (MoE 47B) directly with Ollama using "ollama run mixtral-8x7b-instruct". 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 Mixtral 8x7B (MoE 47B) and NVIDIA GeForce RTX 3070 against alternative configurations:

Recommended Alternative GPUs for Mixtral 8x7B (MoE 47B)

Explore how Mixtral 8x7B (MoE 47B) performs across other budget, midrange, and flagship architectures:

NVIDIA GeForce RTX 5090 (32GB)

31.1 GB req • ~159.7 tok/s

Benchmark

Apple M2 Ultra (192GB Unified) (192GB)

31.1 GB req • ~71.3 tok/s

Benchmark

Other Popular Models You Can Run on NVIDIA GeForce RTX 3070

Verified open-weights LLMs and image models that fit within NVIDIA GeForce RTX 3070's 8GB VRAM:

DeepSeek-R1-Distill-Llama-8B

8B • 7.3 GB VRAM

Simulate

DeepSeek-R1-Distill-Qwen-8B

8.03B • 7.5 GB VRAM

Simulate

DeepSeek-R1-Distill-Qwen-7B

7.6B • 6.5 GB VRAM

Simulate

DeepSeek-R1-Distill-Qwen-1.5B

1.8B • 2.6 GB VRAM

Simulate

Dual GPU Showdown: NVIDIA GeForce RTX 3070 vs NVIDIA GeForce RTX 4090

Compare memory bandwidth (448 GB/s vs 1008 GB/s), VRAM headroom (8GB vs 24GB), and inference speed head-to-head.

Launch Showdown Arena

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.