Internlm 7B — Hardware Requirements & GPU Compatibility
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InternLM-7B is InternLM's open base pretrained language model, not instruction-tuned, built at 7 billion parameters and trained on trillions of high-quality tokens to serve as a general-purpose knowledge foundation for downstream fine-tuning. On the OpenCompass evaluation suite it outperformed same-size peers such as LLaMA-7B and Baichuan-7B across disciplinary, language, knowledge, reasoning, and comprehension benchmarks. A matching InternLM-Chat-7B instruction-tuned version was released alongside it. Its 7B size fits on a single consumer GPU. Context length is 2,048 tokens. The code is released under Apache 2.0, while the model weights are free for academic research and free for commercial use only after completing InternLM's application form. It was published in July 2023, as InternLM's first open base model; larger InternLM2 and later families followed.
Runs well on a 24 GB GPU
Runs from 16 GB (FP16 · 2K)Best on 24 GB (FP16 · ~43 tok/s · 2K)Macs from 24 GB (FP16 · ~14 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Internlm 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve internlm/internlm-7b --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (8 GB headroom) · FP16
- Generation speed
- ~40 tok/s
- generation speed
- Cost per 1M output tokens
- $1.39
- per 1M output tokens
How Much VRAM Does Internlm 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 15.4 GB | — | 16-bit · full — Full half-precision — baseline for inference | Fits: 2K context · ~43 tok/s |
Selected FP16
Full half-precision15.4GBat 2K context
- Weights
- 14.8 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 8.6 GB free · ~43 tok/s
≥50 tok/s needs ≥1.18 TB/s · RTX 5090 ~76
Which Hardware Can Run Internlm 7B?
FP16 · 15.4 GBInternlm 7B (FP16) requires 15.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 122 devices with unified memory can run Internlm 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
16–18 GB
16–18 GB tier →fits · 0.6 GB free · up to 2K ctx
18 GPUs
RTX 5080~41Simulate output on RTX 5080RTX 5070 Ti~38Simulate output on RTX 5070 TiRTX 4080 SUPER~31Simulate output on RTX 4080 SUPER20 GB
fits · 4.6 GB free · up to 2K ctx
1 GPU
RX 7900 XT~31Simulate output on RX 7900 XT32–40 GB
fits · 16.6 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~76Simulate output on RTX 5090M5 Max36 GB~21Simulate output on Mac Studio M5 Max (36 GB)M532 GB~7Simulate output on MacBook Air 13" M5 (32 GB)Orin~9Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~6Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at FP16
How good is Internlm 7B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 7 with a result for this model; badges show who ran the test.
- MMLU
Epoch AI · Jul 2023Lab-reported
Lab-reported51.0%
#61/85 open
- BIG-Bench Hard
Epoch AI · Jul 2023Lab-reported
Lab-reported37.0%
#31/38 open
- GSM8K
Epoch AI · Jul 2023Lab-reported
Lab-reported31.2%
#47/62 open
- HellaSwag
Epoch AI · Jul 2023Lab-reported
Lab-reported70.6%
#33/47 open
- ARC Challenge
Epoch AI · Jul 2023Lab-reported
Lab-reported69.5%
#19/56 open
- BoolQ
Epoch AI · Jul 2023Lab-reported
Lab-reported64.1%
#33/38 open
Alternatives to Internlm 7B
InternLM family · 7 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
Specifications
- Publisher
- InternLM
- Family
- InternLM
- Parameters
- 7B
- Architecture
- InternLMForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 103,168
- Release Date
- 2023-07-06
Frequently Asked Questions
How much VRAM does Internlm 7B need?
Internlm 7B requires 15.4 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 16 bits ÷ 8 = 14 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can I run Internlm 7B on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Internlm 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At FP16 you need at least 15.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Internlm 7B locally?
Yes — Internlm 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Internlm 7B?
At FP16, Internlm 7B can reach ~312 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~43 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 ÷ 15.4 × 0.65 = ~338 tok/s
Estimated speed at FP16 (15.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Internlm 7B?
At FP16, the download is about 14.00 GB.
Which GPUs can run Internlm 7B?
26 consumer GPUs can run Internlm 7B at FP16. 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 7B?
122 devices with unified memory can run Internlm 7B at FP16 (15.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.