Internlm Chat 20B — Hardware Requirements & GPU Compatibility
Chat
Internlm Chat 20B is a 20B-parameter open language model from InternLM in the InternLM family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 14.82 GB of VRAM — see which GPUs and Macs can run it below.
164 downloads
134 likesSpecifications
- Publisher
- InternLM
- Family
- InternLM
- Parameters
- 20B
- Architecture
- InternLMForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 103,168
- Release Date
- 2023-09-18
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Internlm Chat 20B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 11.3 GB | 13.8 GB | 8.50 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 12.6 GB | 15.1 GB | 9.75 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 14.8 GB | 17.3 GB | 12.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 17.1 GB | 19.6 GB | 14.25 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 19.3 GB | 21.8 GB | 16.50 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 22.8 GB | 25.3 GB | 20.00 GB | 8-bit quantization, near-lossless |
| FP16est. | 16.00 | 42.8 GB | 45.3 GB | 40.00 GB | Full half-precision — baseline for inference |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Internlm Chat 20B?
Q4_K_M · 14.8 GBInternlm Chat 20B (Q4_K_M) requires 14.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 4K context window can add up to 2.5 GB, bringing total usage to 17.3 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Internlm Chat 20B?
Q4_K_M · 14.8 GB47 devices with unified memory can run Internlm Chat 20B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Internlm Chat 20B need?
Internlm Chat 20B requires 14.8 GB of VRAM at Q4_K_M, or 42.8 GB at FP16. Full 4K context adds up to 2.5 GB (17.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 4.8 bits ÷ 8 = 12 GB
KV Cache + Overhead ≈ 2.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.3 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M14.8 GBQ4_K_M + full context17.3 GB- Can NVIDIA GeForce RTX 4090 run Internlm Chat 20B?
Yes, at Q8_0 (22.8 GB) or lower. Higher quantizations like FP16 (42.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Internlm Chat 20B?
For Internlm Chat 20B, Q4_K_M (14.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (17.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 11.3 GB.
VRAM requirement by quantization
Q2_K11.3 GBQ4_K_M ★14.8 GBQ5_K_M17.1 GBQ6_K19.3 GBQ8_022.8 GBFP1642.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Internlm Chat 20B on a Mac?
Internlm Chat 20B requires at least 11.3 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Internlm Chat 20B locally?
Yes — Internlm Chat 20B can run locally on consumer hardware. At Q4_K_M quantization it needs 14.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Internlm Chat 20B?
At Q4_K_M, Internlm Chat 20B can reach ~297 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~44 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 14.8 × 0.65 = ~351 tok/s
Estimated speed at Q4_K_M (14.8 GB)
~351 tok/s~44 tok/s~351 tok/s~297 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Internlm Chat 20B?
At Q4_K_M, the download is about 12.00 GB. The full-precision FP16 version is 40.00 GB. The smallest option (Q2_K) is 8.50 GB.
- Which GPUs can run Internlm Chat 20B?
26 consumer GPUs can run Internlm Chat 20B at Q4_K_M (14.8 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Internlm Chat 20B?
49 devices with unified memory can run Internlm Chat 20B at Q4_K_M (14.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.