Q: What quantization of InternLM 2.5 7B should I use on a NVIDIA GeForce GTX 1650 Super?

For 4 GB VRAM on the NVIDIA GeForce GTX 1650 Super, the Q4_K_M variant is the best fit. Estimated ~12 tokens/sec on the Q4_K_M quantization.

Q: How fast does InternLM 2.5 7B run on NVIDIA GeForce GTX 1650 Super?

Roughly 12 tokens/sec for Q4_K_M. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Q: What if NVIDIA GeForce GTX 1650 Super is not enough for InternLM 2.5 7B?

Consider upgrading to NVIDIA GeForce RTX 4060 Ti 8GB (8 GB VRAM) which fits the recommended 8 GB target. Or pick a smaller quantization to stay on your current card.

Question 1

Can I run InternLM 2.5 7B on a NVIDIA GeForce GTX 1650 Super?

Accepted Answer

Sort of — NVIDIA GeForce GTX 1650 Super can run InternLM 2.5 7B (Q4_K_M) only by spilling layers to RAM. Generation will be slow. CPU + GPU hybrid — not enough VRAM (4 GB < 5.5 GB min), but 64 GB RAM is sufficient. Expect significantly slower inference.

Question 2

What quantization of InternLM 2.5 7B should I use on a NVIDIA GeForce GTX 1650 Super?

Accepted Answer

For 4 GB VRAM on the NVIDIA GeForce GTX 1650 Super, the Q4_K_M variant is the best fit. Estimated ~12 tokens/sec on the Q4_K_M quantization.

Question 3

How fast does InternLM 2.5 7B run on NVIDIA GeForce GTX 1650 Super?

Accepted Answer

Roughly 12 tokens/sec for Q4_K_M. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if NVIDIA GeForce GTX 1650 Super is not enough for InternLM 2.5 7B?

Accepted Answer

Consider upgrading to NVIDIA GeForce RTX 4060 Ti 8GB (8 GB VRAM) which fits the recommended 8 GB target. Or pick a smaller quantization to stay on your current card.

Can I Run InternLM 2.5 7B on NVIDIA GeForce GTX 1650 Super?

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Every InternLM 2.5 7B quantization on NVIDIA GeForce GTX 1650 Super

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