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Can the NVIDIA RTX Spark N1X (5120-core GPU) (24 GB) run CogVLM2 Llama 3 Chat 19B?

26Basic quality (D) · 26 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (far over GPU memory limit)

Slow (far over GPU memory limit). At Q2_K it needs about 13.7 GB of 12.0 GB usable, with an estimated speed of 26.4 tok/s.

Best quant
Q2_K
Generation speed
26.4 tok/s
Prompt processing
886 tok/s
First token (1K prompt)
1.2 s
Largest safe context
4K

Download CogVLM2 Llama 3 Chat 19B

Original weights · Hugging Face

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

huggingface.co/zai-org/cogvlm2-llama3-chat-19B (opens in a new tab)

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How it’s rated

Quality 26Basic quality (D) · 26 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 43/100 (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 Slowfar over GPU memory limit
How well it runs on the NVIDIA RTX Spark N1X (5120-core GPU) (24 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.12.0 / 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.4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score43.1 / 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 (5120-core GPU) (24 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 13.7 GB 12.0 GB 26.4 tok/s Slowfar over GPU memory limit 26Basic quality (D) · 26 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 15.4 GB 12.0 GB — Won't run 31Basic quality (D) · 31 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. All files (opens in a new tab)
Q4_K_M 17.7 GB 12.0 GB — Won't run 36Basic quality (D) · 36 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. All files (opens in a new tab)
Q5_K_M 19.7 GB 12.0 GB — Won't run 36Basic quality (D) · 36 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. All files (opens in a new tab)
Q6_K 21.8 GB 12.0 GB — Won't run 37Basic quality (D) · 37 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 26.5 GB 12.0 GB — Won't run 38Basic quality (D) · 38 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 44.9 GB 12.0 GB — Won't run 38Basic quality (D) · 38 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K 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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