BENCHMARK REPORT

Can I Run DeepSeek-R1-Distill-Llama-70B on AMD Instinct MI300X (192GB)?

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: DeepSeek-R1-Distill-Llama-70B on AMD Instinct MI300X (192GB)

Authoritative verdict, memory allocation, and token generation performance:

Direct Answer: Can Run PerfectlyDeepSeek-R1-Distill-Llama-70B runs smoothly with 100% dedicated VRAM allocation on AMD Instinct MI300X (192GB) (192GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 47.60 GB (43.00 GB weights + 2.65 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical GPU memory provides 144.40 GB of free safety buffer, preventing PCIe memory bus throttling. Estimated generation throughput is ~86.3 tokens/sec (~11.6 ms per token) with prompt prefill processing reaching ~3888 tok/s.

Run Verdict

100% Fit (Optimal)

Total Required VRAM

47.60 GB / 192 GB

Decoding Speed

~86.3 tok/s (11.6ms)

Safe Context Window

Up to 32k tokens

Hardware Architecture & VRAM Topology

CDNA 3

AMD Instinct MI300X (192GB) • 5300 GB/s Bandwidth

Token Generation

~86.3 tok/s(11.6 ms)

OPTIMAL

VRAM Utilization Breakdown47.6 GB / 192 GB (25%)

43G

+144.4G Free

Weights

43 GB

KV Cache

2.65 GB

Activation

0.8 GB

OS/Driver

1.15 GB

Free Headroom

144.4 GB

Max Safe Context Limit on this Hardware

You can safely expand prompts up to 131,072 tokens before CUDA OOM.

Headroom Used

8,192 / 131,072

Context Window vs VRAM Scaling Curve

Blue line indicates total VRAM demand across context lengths; Red dashed line is physical VRAM ceiling.Max Safe: 131,072 tokens

Can Run Perfectly

optimal

Entire model & KV cache fit fully into VRAM with pure GPU acceleration.

Throughput

~86.3 tok/s

11.6 ms / token

Headroom

144.4 GB

25% of capacity

Layers Fit

80 / 80

100% in VRAM

Max Safe Ctx

128k

Tokens before OOM

Static Permalink:/can-i-run/deepseek-r1-distill-llama-70b-on-instinct-mi300x

Open Static Report

Ready-to-Run CLI Commands

ollama run deepseek-r1:70b

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

Cluster Scaled Serving

gpuy.dev On-Demand Compute

Fine-Tune or Scale DeepSeek-R1-Distill-Llama-70B on 2x NVIDIA RTX 4090 (48GB)

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 $0.72/hr.

2x Faster Speed

$0.72/hr

Breakeven: ~5,278 hrs vs hardware purchase

Per-second billing

Launch Cloud Node →Pre-configured environment

Hardware Specification Summary

GPU ArchitectureCDNA 3

Memory Bandwidth5300 GB/s

Model Parameters70.6B (LLM)

Attention ArchitectureGrouped-Query (GQA)

Prompt Prefill Speed~3888 tok/s (TTFT ~2107ms)

Benchmark Context Tested8,192 tokens

Customize in Full Matcher Simulator →

Global Compatibility Matrix for AMD Instinct MI300X (192GB)

Evaluated at Standard Q4_K_M Precision with 8,192 Tokens Context Window

Compatibility Index

95% (54/57)

54 Pass0 Tight3 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 (-219.16G)	~34.7 tok/s	Simulate
DeepSeek-V3 (671B Base/Chat)MLAMoE DeepSeek	MoE	671B(37B active)	411.16 GB	OOM (-219.16G)	~34.7 tok/s	Simulate
DeepSeek-R1-Distill-Llama-70B DeepSeek	LLM	70.6B	47.6 GB	100% VRAM Fit	~86.3 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-32B DeepSeek	LLM	32.8B	23.75 GB	100% VRAM Fit	~180 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-14B DeepSeek	LLM	14.7B	11.94 GB	100% VRAM Fit	~180 tok/s	Simulate
DeepSeek-R1-Distill-Llama-8B DeepSeek	LLM	8B	7.33 GB	100% VRAM Fit	~180 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-8B DeepSeek	LLM	8.03B	7.48 GB	100% VRAM Fit	~180 tok/s	Simulate
DeepSeek-R1-Distill-Qwen-7B DeepSeek	LLM	7.6B	6.49 GB	100% VRAM Fit	~180 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	100% VRAM Fit	~180 tok/s	Simulate
Qwen 2.5 Coder 72B Instruct Qwen	LLM	72.7B	48.87 GB	100% VRAM Fit	~83.8 tok/s	Simulate
Qwen 2.5 72B Instruct Qwen	LLM	72.7B	48.87 GB	100% VRAM Fit	~83.8 tok/s	Simulate
Qwen 2.5 VL 72B (Vision Flagship) Qwen	LLM	72.7B	48.87 GB	100% VRAM Fit	~83.8 tok/s	Simulate
Qwen 2.5 VL 7B (Vision) Qwen	LLM	7.61B	6.5 GB	100% VRAM Fit	~180 tok/s	Simulate
Qwen 2.5 Coder 32B Instruct Qwen	LLM	32.8B	23.75 GB	100% VRAM Fit	~180 tok/s	Simulate
Qwen 2.5 Coder 14B Instruct Qwen	LLM	14.7B	11.94 GB	100% VRAM Fit	~180 tok/s	Simulate
Qwen 2.5 Coder 7B Instruct Qwen	LLM	7.6B	6.49 GB	100% VRAM Fit	~180 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	100% VRAM Fit	~180 tok/s	Simulate
Qwen 2.5 14B Instruct Qwen	LLM	14.7B	11.94 GB	100% VRAM Fit	~180 tok/s	Simulate
Qwen 2.5 7B Instruct Qwen	LLM	7.6B	6.49 GB	100% VRAM Fit	~180 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	100% VRAM Fit	~86.3 tok/s	Simulate
Llama 3.1 70B Instruct Llama	LLM	70.6B	47.6 GB	100% VRAM Fit	~86.3 tok/s	Simulate
Llama 3.1 405B Instruct Llama	LLM	405B	252.77 GB	OOM (-60.77G)	~5.1 tok/s	Simulate
Llama 3.1 8B Instruct Llama	LLM	8B	7.33 GB	100% VRAM Fit	~180 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	~180 tok/s	Simulate
Llama 3.2 90B Vision Instruct Llama	LLM	89B	59.46 GB	100% VRAM Fit	~68.4 tok/s	Simulate
Microsoft Phi-4 (14B) Phi	LLM	14.7B	12.01 GB	100% VRAM Fit	~180 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	~180 tok/s	Simulate
Mistral Small 3 (24B Instruct) Mistral	LLM	24B	18.12 GB	100% VRAM Fit	~180 tok/s	Simulate
Mistral NeMo (12B Instruct) Mistral	LLM	12.2B	10.15 GB	100% VRAM Fit	~180 tok/s	Simulate
Mistral 7B Instruct v0.3 Mistral	LLM	7.25B	6.88 GB	100% VRAM Fit	~180 tok/s	Simulate
Codestral 22B (Mistral AI) Mistral	LLM	22.2B	17.02 GB	100% VRAM Fit	~180 tok/s	Simulate
Mistral Large 2 (123B) Mistral	LLM	123B	79.77 GB	100% VRAM Fit	~49.5 tok/s	Simulate
Mixtral 8x7B (MoE 47B)MoE Mistral	MoE	46.7B(12.9B active)	31.15 GB	100% VRAM Fit	~180 tok/s	Simulate
Mixtral 8x22B (MoE 141B)MoE Mistral	MoE	141B(39B active)	89.67 GB	100% VRAM Fit	~156.2 tok/s	Simulate
Pixtral 12B (Multimodal) Mistral	LLM	12.4B	10.28 GB	100% VRAM Fit	~180 tok/s	Simulate
Gemma 2 27B Instruct Gemma	LLM	27.2B	21.26 GB	100% VRAM Fit	~180 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	~180 tok/s	Simulate
FLUX.1 [dev] (12B DiT) FLUX	ImageGen	12B	15.68 GB	100% VRAM Fit	~180 tok/s	Simulate
FLUX.1 [schnell] (4-step) FLUX	ImageGen	12B	15.68 GB	100% VRAM Fit	~180 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	~180 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	~180 tok/s	Simulate
Stable Diffusion 3.5 Large (8B) SD	ImageGen	8.1B	13.3 GB	100% VRAM Fit	~180 tok/s	Simulate
BAAI BGE-M3 (Multilingual Embedding) BAAI	LLM	0.56B	2.54 GB	100% VRAM Fit	~180 tok/s	Simulate

DeepSeek-R1-Distill-Llama-70B Context Scaling Matrix on AMD Instinct MI300X (192GB)

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)	0.66 GB	45.61 GB / 192 GB	100% Fit	0 (All in VRAM)	~86.3 tok/s
4,096 tokens (4k)	1.33 GB	46.27 GB / 192 GB	100% Fit	0 (All in VRAM)	~86.3 tok/s
8,192 tokens (8k)Standard Baseline	2.65 GB	47.60 GB / 192 GB	100% Fit	0 (All in VRAM)	~86.3 tok/s
16,384 tokens (16k)	5.30 GB	50.25 GB / 192 GB	100% Fit	0 (All in VRAM)	~86.3 tok/s
32,768 tokens (32k)	10.60 GB	55.55 GB / 192 GB	100% Fit	0 (All in VRAM)	~86.3 tok/s

Hardware Compatibility Q&A: DeepSeek-R1-Distill-Llama-70B on AMD Instinct MI300X (192GB)

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

Can AMD Instinct MI300X (192GB) run DeepSeek-R1-Distill-Llama-70B?

Can Run Perfectly. Entire model & KV cache fit fully into VRAM with pure GPU acceleration. At an 8k context window, total VRAM demand is 47.6 GB compared to AMD Instinct MI300X (192GB)'s 192 GB frame buffer.

How much VRAM does DeepSeek-R1-Distill-Llama-70B require?

At standard Q4_K_M quantization and 8k context, DeepSeek-R1-Distill-Llama-70B requires approximately 47.6 GB of VRAM. This includes 43 GB for model weights, 2.65 GB for dynamic KV cache (Grouped-Query Attention (GQA)), 0.8 GB for activation buffers, and 1.15 GB for OS display and CUDA runtime overhead.

What generation speed (tokens/sec) can I expect on AMD Instinct MI300X (192GB)?

Estimated autoregressive token generation speed is ~86.3 tokens per second with a per-token latency of ~11.6 ms, powered by 5300 GB/s memory bandwidth. Prompt prefill processing reaches ~3888 tok/s (TTFT ~2107 ms).

What causes CUDA Out-of-Memory (OOM) when running DeepSeek-R1-Distill-Llama-70B?

CUDA OOM occurs when total memory consumption exceeds AMD Instinct MI300X (192GB)'s 192 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 DeepSeek-R1-Distill-Llama-70B locally on AMD Instinct MI300X (192GB)?

You can run DeepSeek-R1-Distill-Llama-70B directly with Ollama using "ollama run deepseek-r1-distill-llama-70b". 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 DeepSeek-R1-Distill-Llama-70B and AMD Instinct MI300X (192GB) against alternative configurations:

Recommended Alternative GPUs for DeepSeek-R1-Distill-Llama-70B

Explore how DeepSeek-R1-Distill-Llama-70B performs across other budget, midrange, and flagship architectures:

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

47.6 GB req • ~13 tok/s

Benchmark

Other Popular Models You Can Run on AMD Instinct MI300X (192GB)

Verified open-weights LLMs and image models that fit within AMD Instinct MI300X (192GB)'s 192GB VRAM:

DeepSeek-R1-Distill-Qwen-32B

32.8B • 23.8 GB VRAM

Simulate

DeepSeek-R1-Distill-Qwen-14B

14.7B • 11.9 GB VRAM

Simulate

DeepSeek-R1-Distill-Llama-8B

8B • 7.3 GB VRAM

Simulate

DeepSeek-R1-Distill-Qwen-8B

8.03B • 7.5 GB VRAM

Simulate

Dual GPU Showdown: AMD Instinct MI300X (192GB) vs NVIDIA GeForce RTX 4090

Compare memory bandwidth (5300 GB/s vs 1008 GB/s), VRAM headroom (192GB 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.