Model datasheet · Gemma 4
Gemma 4 E4B
Phone-class Gemma: Per-Layer Embeddings mean 8B of weights behave like 4.5B at inference, and the official QAT build is 5.15GB. Text, image and audio in the 8GB tier — but the benchmarks are honest about the ceiling.
- Vendor
- Architecture
- Dense · 8B
- Context
- 131,072 tokens
- License
- Apache-2.0
- Released
- 2026-07-02
- Vision
- Yes
→ GGUF (llama.cpp / LM Studio / Ollama) → MLX (Apple Silicon)
§1 Characteristics by quantization band
— Not yet rated · contributions welcome —
Q4–Q5 — 4–5 bit
5GB weights · e.g. Q4_0 , Q4_K_M , mlx-4bit + KV: 132MB at 8K · 420MB at 32K · 1.5GB at 128Kdata checked Aug 2026
- quant-degradation-community — community consensus @ Q4_0 · aggregated · 2026-08-30
Editorial derivation: QAT Q4_0 (5.15GB) is the intended build, but small models have less redundancy to lose — one point down.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q4_0 · aggregated · 2026-08-30
Editorial derivation: carried down one point from the full band.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q4_0 · aggregated · 2026-08-30
Editorial derivation: an already-marginal tool layer at 4-bit. Treat tool use as unsupported here.
— Not yet rated · contributions welcome —
FP16 — full precision (fp16/bf16)
16GB weights · e.g. bf16 + KV: 132MB at 8K · 420MB at 32K · 1.5GB at 128Kdata checked Aug 2026
- vendor-model-card — AIME 2026 (no tools) = 42.5 @ bf16 · vendor-reported · 2026-08-30
GPQA Diamond: 58.6, MMLU-Pro: 69.4. Strong for 4.5B effective parameters, weak in absolute terms.
data checked Aug 2026
- vendor-model-card — LiveCodeBench v6 = 52 @ bf16 · vendor-reported · 2026-08-30
Competition-style problems only; no agentic or repo-level evidence at this size.
data checked Aug 2026
- vendor-model-card — Tau2 average (with tools) = 42.2 @ bf16 · vendor-reported · 2026-08-30
Half the 12B's score. Usable for one or two well-described tools, not for agent loops.
data checked Aug 2026
- vendor-model-card — MRCR v2 @128K (8 needle) = 25.4 @ bf16 · vendor-reported · 2026-08-30
The 128K window is nominal — a quarter of needles found.
§2 Known issues & what fixes them
Tau2 42.2 against the 12B's 69.0 — by the vendor's own measurement, roughly two in five tool-use episodes go wrong. This is a capacity ceiling at 4.5B effective parameters, not a bug, and no runtime update moves it.
One or two tools with unambiguous schemas, single-step calls, and validate every argument before executing. Multi-step agent loops are out of reach at this size.
If the workload genuinely needs reliable tool use, move up to Gemma 4 12B (Tau2 69.0) — a 9GB rather than 7GB footprint.
Evidence · 1 source · anecdotal
- Gemma 4 model card — Tau2 per variant (E2B 24.5, E4B 42.2, 12B 69.0) (vendor, 2026-08-30)
The 128K context window is nominal: MRCR v2 at 128K is 25.4%, so three quarters of the needles are missed. Loading a long document and asking questions about it will silently produce confident, wrong answers.
Chunk and retrieve — feed it a page, not a book. This is the single highest-value mitigation for the E-series.
Evidence · 1 source · anecdotal
- Gemma 4 model card — MRCR v2 @128K per variant (vendor, 2026-08-30)
Inherits the family's local-runtime churn — official GGUF refreshes that would not load, and gibberish past ~230 tokens with KV offload enabled.
Current llama.cpp, a re-cut community quant, and no -nkvo.
Evidence · 2 sources · community-consensus
- Eval bug: Gemma4 models produce gibberish at some point with -nkvo (github, 2026-08-30)
- Latest official Google Gemma 4 GGUF models fail to load in LM Studio 0.4.19 (github, 2026-08-30)