Gemma 4 on RTX 4070 Context Limits | Generated by AI
Question: What context size can we set for the unsloth/gemma-4-E4B-it-GGUF model on an RTX 4070 using llama.cpp in OpenClaw, and how about 32K?
Answer:
The unsloth/gemma-4-E4B-it-GGUF is a quantized GGUF version of Google’s Gemma 4 E4B (Efficient 4B) instruct-tuned model. This is a compact dense model with roughly 4–4.5 billion effective parameters (around 8B including embeddings). It is designed for efficient local inference and supports a native maximum context length of 128K tokens (not 256K, which is reserved for the larger Gemma 4 variants like the 26B-A4B MoE or 31B models).
Recommended Context Size on RTX 4070 (12 GB VRAM) with llama.cpp
- Model loading: A typical Q4_K_M or Q4_K_S GGUF quantization of this ~4B-class model uses only ~3–6 GB VRAM for the weights themselves (depending on the exact quant and whether you offload all layers to GPU). This leaves plenty of headroom on your 12 GB RTX 4070.
- KV cache impact: The KV (key-value) cache is the main factor that grows with context size. Gemma 4 uses a sliding window attention (512 tokens) plus full attention mechanisms, which makes the cache somewhat more memory-intensive than standard Llama-style models at very long contexts. However, for a small 4B model the overhead remains manageable.
- Practical limits:
- 8K–16K context: Very comfortable. Excellent speed and low VRAM usage (total often under 8 GB).
- 32K context: Yes, this should work well. On a 12 GB GPU like the RTX 4070, a Q4 quant of Gemma 4 E4B at 32K context typically stays within 8–10 GB total VRAM (including model + KV cache + overhead). Many users run similar small quantized models at 32K without issues.
- 64K context: Possible but tighter. You may need to use lower KV cache quantization (
--cache-type-k q4_0orq8_0with some trade-offs) or reduce batch size. Test it — it often fits but leaves less margin for safety. - Higher (up to 128K): Not recommended on 12 GB VRAM with full GPU offload. The KV cache would likely push total usage over 12 GB and cause out-of-memory errors.
Tips for Running in OpenClaw with llama.cpp
OpenClaw integrates with llama.cpp (often via an OpenAI-compatible server like llama-server). Use these flags for best results:
--ctx-size 32768(or 16384 to start safely)--n-gpu-layers 99(or a high number to fully offload the small model)--cache-type-k q4_0 --cache-type-v q4_0(reduces KV cache VRAM significantly with minor quality loss)--flash-attn(if supported in your llama.cpp build — helps with speed and memory)-np 1(if running single-user; reduces sliding-window cache overhead)- Monitor VRAM with
nvidia-smiwhile starting the server.
Start with 32K — it is a good balance of capability and stability on your hardware. If you get out-of-memory errors, drop to 16K–24K or apply heavier cache quantization. The model itself is very lightweight, so performance (tokens/second) should remain fast even at 32K.
The native training context is 128K, so the model can handle 32K without any architectural problems. Just ensure your llama.cpp build is recent enough to fully support Gemma 4 (most builds from the last few days do).
In short: 32K context is realistic and recommended on an RTX 4070 with this model in OpenClaw/llama.cpp. You should get reliable performance with room to spare.
References:
- https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF
- https://ai.google.dev/gemma/docs/core/model_card_4 (Gemma 4 context specs)
- Community reports on llama.cpp VRAM usage for small Gemma 4 models on 12 GB GPUs.