Gemma-4-26B-A4B-IT — UD-Q5_K_XL (mlx-node)
| Original (BF16) | UD-Q5_K_XL (this model) |
|---|
| Size | ~49 GB | 20 GB |
| Format | SafeTensors | SafeTensors |
| Precision | BF16 uniform | 5-bit affine + mixed bits + BF16 |
| FFN group size | — | 64 |
| Biases | — | yes |
All Variants
Benchmarked on Apple M3 Max 128GB via
examples/lm.ts (best decode tok/s across turns 2–4, steady-state, capitals chat with
reasoningEffort: 'low').
Note: No Q2 variant is published — Gemma-4-26B-A4B-IT has only ~4B active parameters per token, which is below the architectural redundancy needed for 2-bit quantization to remain coherent. Both unsloth and mixed_2_6 recipes produced gibberish at Q2 on this model.
Performance
Steady-state decode: 50.3 tok/s on Apple M3 Max 128GB (best of turns 2–4, examples/lm.ts capitals chat with reasoningEffort: 'low'). Decode is memory-bandwidth bound on Apple Silicon — fewer bytes per token directly translates to higher throughput. The MoE architecture activates only top-K of 128 experts per token (~4B active out of ~26B total), and the compiled C++ forward graph fuses the per-layer dispatch.
Per-Tensor Bit Assignments (N=5)
| Weight | Mode | Bits | Group | Rationale |
|---|
embed_tokens | 8-bit affine | 8 | 64 | Tied with lm_head (Gemma4 shares weights); affine-only loader |
self_attn.q_proj | 8-bit affine | 8 | 64 | AWQ-corrected via input_layernorm |
self_attn.k_proj | 8-bit affine | 8 | 64 | AWQ-corrected via input_layernorm |
self_attn.v_proj | 8-bit affine | 8 | 64 | AWQ-corrected via input_layernorm (only on full-attention layers) |
mlp.gate_proj | 5-bit affine | 5 | 64 | Shared dense MLP (top-level default) |
mlp.up_proj | 5-bit affine | 5 | 64 | Shared dense MLP (top-level default) |
mlp.down_proj | 6-bit affine | 6 | 64 | Shared dense MLP; "slightly more sensitive" (unsloth base+1) |
experts.switch_glu.gate_proj | 5-bit affine | 5 | 64 | MoE expert gate (per-expert across all 128); base bits (top-level default) |
experts.switch_glu.up_proj | 5-bit affine | 5 | 64 | MoE expert up (per-expert across all 128); base bits (top-level default) |
experts.switch_glu.down_proj | 6-bit affine | 6 | 64 | MoE expert down (per-expert across all 128 + routing); unsloth base+1 |
router.proj | 8-bit affine | 8 | 64 | MoE routing — low-bit noise breaks top-K expert selection |
self_attn.o_proj | bf16 | — | — | NOT AWQ-correctable; kept full-precision |
Quantization Strategy
Built on Unsloth Dynamic 2.0 per-tensor KLD analysis. At --q-bits 5 the unsloth recipe assigns the base bits to MLP gate/up projections (the bulk of the parameter budget), base+1 to down_proj (slightly more sensitive), base+2 (snapped to a valid bit width) + AWQ pre-scaling to attention q/k/v projections, base+2 to embed_tokens, base+3 (capped/snapped) to the routing-critical paths, and keeps self_attn.o_proj as bf16 (AWQ-uncorrectable — its inputs come from the attention compute, not from a norm layer). The MoE router (router.proj) is forced to 8-bit affine to preserve top-K expert selection accuracy.
imatrix AWQ pre-scaling amplifies important weight channels and fuses inverse scales into preceding layer norms (zero inference overhead).
Architecture
| Parameter | Value |
|---|
| Total parameters | ~26B (~4B active per token) |
| Hidden size | 2,816 |
| Layers | 30 (sliding-window attention) |
| Attention heads | 16 (8 KV heads, GQA 2:1) |
| Head dimension | 256 |
| Experts | 128 per MoE layer |
| MoE intermediate size | 704 |
| Vocab size | 262,144 |
| Max context | 262,144 tokens |
| Vision | yes (Gemma4ForConditionalGeneration) |
Usage
1import { loadSession } from '@mlx-node/lm';
2
3const session = await loadSession('./Gemma-4-26B-A4B-IT-UD-Q5_K_XL-mlx');
4
5for await (const event of session.sendStream('Explain the MoE architecture in Gemma-4.', {
6 config: { maxNewTokens: 2048, temperature: 0.6, reasoningEffort: 'low' },
7})) {
8 if (!event.done) process.stdout.write(event.text);
9}
How It Was Made
1mlx convert \
2 -i gemma-4-26b-a4b-it \
3 -o Gemma-4-26B-A4B-IT-UD-Q5_K_XL-mlx \
4 -q --q-bits 5 --q-recipe unsloth \
5 --imatrix-path imatrix_unsloth.gguf
Acknowledgments
- Unsloth — Quantization strategy based on their per-layer KLD benchmarks and Dynamic 2.0 methodology
- Google DeepMind — For the Gemma-4 model family
- Apple MLX — For the Metal-accelerated ML framework
License
Gemma Terms of Use (inherited from base model).
