👁 InternLM
InternLM
InternVL2 8B
Current
70.0KDownloads187LikesJul 2024Released8K tokensContextMITLicense76 StrongQuality
InternVL2 8B (8B parameters) requires approximately 8.6 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 10 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run InternVL2 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "OpenGVLab/InternVL2-8B" \
--hf-file "InternVL2-8B-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters8B
Architecturedense
Context8K tokens
Modalitytext+vision
Min RAM3.1 GB
Rec. RAM4.9 GB (Q4_K_M)
LicenseMIT
FamilyInternVL
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About this model
- •For more details and evaluation reproduction, please refer to our Evaluation Guide
- •We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA,...
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Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | — |
Q3_K_S | 3 | 3.9 GB | Low | — |
NVFP4 | 4 | 4.5 GB | Medium | — |
Q4_K_M | 4 | 4.9 GB | Medium | — |
Q5_K_M | 5 | 5.8 GB | High | — |
Q6_K | 6 | 6.6 GB | High | — |
Q8_0 | 8 | 8.6 GB | Very High | — |
F16 | 16 | 16.4 GB | Maximum | — |
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — InternVL2 8B
See also