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Can the NVIDIA RTX Spark N1X (6144-core GPU) (128 GB) run Llama 2 70B Chat?

34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly

Slow. At Q4_K_M it needs about 52.2 GB of 102 GB usable, with an estimated speed of 5.3 tok/s.

Best quant
Q4_K_M
Generation speed
5.3 tok/s
Prompt processing
301 tok/s
First token (1K prompt)
3.4 s
Largest safe context
4K

Best app to run it on the NVIDIA RTX Spark N1X (6144-core GPU) (128 GB)

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

  • One command with Ollama’s own build of this model.
  • Serves an OpenAI-compatible API that other apps and coding tools can use.
ollama run llama2:70b-chat-q4_K_M
  • vLLM The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
  • LM Studio Point-and-click: search, download and chat in one window.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Llama 2 70B Chat

Recommended for the NVIDIA RTX Spark N1X (6144-core GPU) (128 GB): Q4_K_M. 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-q4_K_M

Quantized files · GGUF

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

Download the Q4_K_M 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:Q4_K_M

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 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, 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 Slow
How well it runs on the NVIDIA RTX Spark N1X (6144-core GPU) (128 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.15.4 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.5.5 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.11.8 / 15
Match score57.6 / 100

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

Every quantization on the NVIDIA RTX Spark N1X (6144-core GPU) (128 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 37.7 GB 102 GB 8.8 tok/s Slow 25Basic quality (D) · 25 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 43.9 GB 102 GB 6.9 tok/s Slow 30Basic quality (D) · 30 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 52.2 GB 102 GB 5.3 tok/s Slow 34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 59.1 GB 102 GB 4.4 tok/s Slow 34Basic quality (D) · 34 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 66.7 GB 102 GB 3.7 tok/s Slow 35Basic quality (D) · 35 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.
Q8_0 83.3 GB 102 GB 2.7 tok/s Very slow 36Basic quality (D) · 36 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.
F16 148 GB 102 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.

Other hardware that runs it

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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