InternLM2 Chat 20B on NVIDIA RTX 6000 Ada Generation

Can the NVIDIA RTX 6000 Ada Generation run InternLM2 Chat 20B?

Yes, it runs great

Runs great. At Q6_K it needs about 20.9 GB of 48.0 GB usable, with an estimated speed of 41.2 tok/s.

Best quant: Q6_K
Generation speed: 41.2 tok/s
Prompt processing: 3,303 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 32K

Best app to run it on the NVIDIA RTX 6000 Ada Generation

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

Point-and-click: search, download and chat in one window.

Open InternLM2 Chat 20B in LM Studio

llama.cpp Usually the first to support new model architectures.
Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

On Linux

vLLM

Inference server · runs on 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.

vllm serve internlm/internlm2-chat-20b

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

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 InternLM2 Chat 20B

Recommended for the NVIDIA RTX 6000 Ada Generation: Q6_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q6_K file (opens in a new tab)

huggingface.co/mradermacher/internlm2-chat-20b-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/internlm2-chat-20b-i1-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/internlm/internlm2-chat-20b (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q6_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 Runs great: How well it runs on the NVIDIA RTX 6000 Ada Generation: 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.	18.7 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	10.0 / 15
Match score	68.8 / 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 6000 Ada Generation

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	12.6 GB	48.0 GB	97.5 tok/s	Runs great	29Basic quality (D) · 29 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	14.3 GB	48.0 GB	76.7 tok/s	Runs great	35Basic quality (D) · 35 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	16.7 GB	48.0 GB	59.3 tok/s	Runs great	39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	18.7 GB	48.0 GB	49.0 tok/s	Runs great	40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	20.9 GB	48.0 GB	41.2 tok/s	Runs great	41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	25.7 GB	48.0 GB	30.2 tok/s	Runs great	42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`F16`	44.3 GB	48.0 GB	13.9 tok/s	Runs welltight fit	42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 66/100 (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

42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q8_0 · 56.4 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q6_K · 40.1 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q6_K · 43.2 tok/s · Runs great
40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q5_K_M · 42.2 tok/s · Runs great
39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q4_K_M · 41.5 tok/s · Runs well (tight fit)
39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q4_K_M · 45.5 tok/s · Runs well (tight fit)

Quick answers

How much memory does InternLM2 Chat 20B need on the NVIDIA RTX 6000 Ada Generation 48GB?

At Q6_K it needs about 20.9 GB. The NVIDIA RTX 6000 Ada Generation 48GB has 48.0 GB usable.

How fast is InternLM2 Chat 20B on the NVIDIA RTX 6000 Ada Generation 48GB?

About 41.2 tok/s at Q6_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of InternLM2 Chat 20B should I use on the NVIDIA RTX 6000 Ada Generation 48GB?

Q6_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.