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Can the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (4 GB) run Llama 2 70B Chat?

25Basic quality (D) · 25 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. Llama 2 70B Chat needs about 29.7 GB even at Q2_K; the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (4 GB) has 2.0 GB usable.

Download Llama 2 70B Chat

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run llama2:70b-chat-q2_K

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K file (opens in a new tab)

huggingface.co/TheBloke/Llama-2-70B-Chat-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/Llama-2-70B-Chat-GGUF:Q2_K

Original weights · Hugging Face

Full-precision files from Meta, for fine-tuning, vLLM, Transformers.

huggingface.co/meta-llama/Llama-2-70b-chat-hf (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Meta. The weights themselves download from Hugging Face.

github.com/meta-llama/llama (opens in a new tab)

Meta’s original Llama 2 repo (now archived).

git clone https://github.com/meta-llama/llama

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 25Basic quality (D) · 25 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Won't run
How well it runs on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (4 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.3.4 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Match score3.4 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 29.7 GB 2.0 GB — Won't run 25Basic quality (D) · 25 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 35.9 GB 2.0 GB — Won't run 30Basic quality (D) · 30 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 44.2 GB 2.0 GB — Won't run 34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 51.1 GB 2.0 GB — Won't run 34Basic quality (D) · 34 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 58.7 GB 2.0 GB — Won't run 35Basic quality (D) · 35 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking.
Q8_0 75.2 GB 2.0 GB — Won't run 36Basic quality (D) · 36 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking.
F16 140 GB 2.0 GB — Won't run 36Basic quality (D) · 36 of 100: how capable the model is at F16, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

Models that do run on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (4 GB)

Quick answers

How much memory does Llama 2 70B Chat need on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS 4GB?

Even at Q2_K it needs about 29.7 GB; the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS 4GB has 2.0 GB usable.

What can the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS 4GB run instead?

LFM3 1.7B, PaddleOCR-VL 1.6 0.9B and MiniCPM-OCR 1.0 1.3B run best on it; the page lists more, each with its speed.

What hardware can run Llama 2 70B Chat?

NVIDIA RTX PRO 6000 Blackwell 96GB, RTX 5090 32GB and M4 Max 64GB run it; the page lists more, each with its speed.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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