Internlm 20B — Hardware Requirements & GPU Compatibility
Chat
InternLM-20B is a 20-billion-parameter base language model released by the Shanghai Artificial Intelligence Laboratory with SenseTime, the Chinese University of Hong Kong, and Fudan University, pretrained on over 2.3 trillion tokens of English, Chinese, and code data. It is a pretrained model, not instruction-tuned; a separate InternLM-20B-Chat checkpoint underwent additional SFT and RLHF. Unlike typical 13B models that use 32-40 layers, it uses an unusually deep 60-layer architecture, which the card credits for its strong language, reasoning, and comprehension scores against Llama-13B, Llama2-13B, and Baichuan2-13B, approaching some 33B-70B models on several benchmarks. As an early open Chinese LLM from 2023, it predates today's more capable open models of similar size, and it needs a high-end consumer GPU or multi-GPU setup to run comfortably. Context length is 4,096 tokens, though the card states it can support 16,384 tokens through inference-time extrapolation. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2023.
460 downloads
77 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 20B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 42.8 GB | 45.3 GB | 40.00 GB | Brain floating point 16 — preferred for training |
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 20B?
BF16 · 42.8 GBInternlm 20B (BF16) requires 42.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 56+ GB is recommended. Using the full 4K context window can add up to 2.5 GB, bringing total usage to 45.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Internlm 20B?
BF16 · 42.8 GB27 devices with unified memory can run Internlm 20B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Internlm 20B need?
Internlm 20B requires 42.8 GB of VRAM at BF16. Full 4K context adds up to 2.5 GB (45.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 16 bits ÷ 8 = 40 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
BF1642.8 GBBF16 + full context45.3 GB- Can NVIDIA GeForce RTX 5090 run Internlm 20B?
No — Internlm 20B requires at least 42.8 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Internlm 20B on a Mac?
Internlm 20B requires at least 42.8 GB at BF16, 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 20B locally?
Yes — Internlm 20B can run locally on consumer hardware. At BF16 quantization it needs 42.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Internlm 20B?
At BF16, Internlm 20B can reach ~112 tok/s on AMD Instinct MI350X. 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 ÷ 42.8 × 0.65 = ~121 tok/s
Estimated speed at BF16 (42.8 GB)
~121 tok/s~121 tok/s~112 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Internlm 20B?
At BF16, the download is about 40.00 GB.
- Which GPUs can run Internlm 20B?
No single consumer GPU has enough VRAM to run Internlm 20B at BF16 (42.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Internlm 20B?
27 devices with unified memory can run Internlm 20B at BF16 (42.8 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.