RTX 4070 vLLM GGUF Limitation | Generated by AI
Question: Can gemma-4-26B-A4B-it-UD-IQ3_S.gguf be served by vLLM on a local RTX 4070 server?
Answer:
Short answer: No.
You cannot serve gemma-4-26B-A4B-it-UD-IQ3_S.gguf with vLLM on an RTX 4070 (12GB).
There are two separate blockers:
1. vLLM does NOT support GGUF models
Your model:
gemma-4-26B-A4B-it-UD-IQ3_S.gguf
is:
- GGUF format
- llama.cpp quantization (IQ3_S)
- Unsloth / llama.cpp ecosystem
But vLLM only supports:
- HuggingFace FP16/BF16
- AWQ
- GPTQ
- some tensor-parallel sharded weights
vLLM cannot load GGUF at all.
So even before VRAM — it simply won’t run.
You must use one of:
- llama.cpp
- llama-cpp-python
- Ollama
- OpenWebUI + llama.cpp backend
- OpenClaw (if using llama backend)
2. RTX 4070 VRAM is too small anyway
RTX 4070:
12GB VRAM
Your model:
Gemma 4 26B IQ3_S
Approx VRAM usage:
| Quant | VRAM needed |
|---|---|
| IQ3_S | ~13–15GB |
| IQ2 | ~10–11GB |
| Q4_K_M | ~18–20GB |
So:
- IQ3_S → too big for 4070
- might work CPU offload, but slow
- vLLM cannot do GGUF CPU offload anyway
What WILL work on RTX 4070
Best choices:
Option 1 (recommended)
Use llama.cpp:
llama-server \
-m gemma-4-26B-A4B-it-UD-IQ3_S.gguf \
--n-gpu-layers 30
This:
- partially loads GPU
- rest CPU
- usable speed
Option 2 (better fit)
Use smaller quant:
IQ2_XS
IQ2_S
These fit fully in 4070.
Option 3 (best for vLLM)
Use non-GGUF model:
Example:
gemma-2-27b-it AWQ
gemma-2-9b-it
qwen3-14b-awq
These can run in vLLM.
Final Verdict
| Question | Answer |
|---|---|
| vLLM support GGUF | ❌ No |
| RTX 4070 fits IQ3_S | ❌ No |
| llama.cpp works | ✅ Yes |
| CPU offload possible | ✅ Yes (slow) |
| Best choice | smaller IQ2 or 14B model |
If you tell me:
- vLLM only?
- OpenClaw?
- Ollama?
- llama.cpp?
I’ll give the best model for RTX 4070.