📱 Run it on your phone or a GPU-less PC → POCKET · 🚀 Try it live (CPU chat)
VIDRAFT's on-device family: a 35B model that runs on iPhone and on CPU with no GPU — stock llama.cpp, no fork.
Ourbox-35B-JGOS — GGUF (consumer / edge)
GGUF quantizations of Ourbox-35B-JGOS — a 34.7B-total / ~3B-active (A3B) sparse Mixture-of-Experts reasoning model (Qwen3.5-MoE / Qwen3-Next family: Gated-DeltaNet linear attention interleaved with full attention, 256 experts top-8).
These files are built to run a 35B-class reasoner on ordinary consumer hardware — including an 8 GB gaming laptop.
Highlight — measured on a gaming laptop
20.01 tok/s decode for a 34.7B model on an RTX 5060 Laptop GPU (8 GB VRAM) + an AVX2-only laptop CPU.
Coherent chain-of-thought output. Measured with llama-bench (tg64, stable ± 0.24).
The whole point of an A3B model is that decode cost scales with active parameters (~3B), not total (34.7B). So the experts sit in system RAM, only attention/router/shared layers occupy the GPU, and per token the machine moves ~1.45 GB instead of a dense 34B's ~16.7 GB — about 11× less memory traffic.
Same weights, both extremes (measured)
The identical model spans the entire hardware spectrum:
| Tier | Hardware | Throughput | Serving |
|---|
| Datacenter ceiling | single B200 | 18,057 tok/s aggregate | VIDRAFT optimized serving (VKAE) |
| Consumer floor | 8 GB laptop (RTX 5060) | 20.01 tok/s single-stream | open llama.cpp, Q3_K_M (VKUE) |
Both numbers are measured. One set of weights, from a datacenter B200 down to a gaming laptop.
Files
| File | Quant | Size | Notes |
|---|
ourbox35b-Q3_K_M.gguf | Q3_K_M (~3.9 bpw) | 16.8 GB | recommended for 8–12 GB VRAM + 24–32 GB RAM |
ourbox35b-Q4_K_M.gguf | Q4_K_M (~4.5 bpw) | 21.2 GB | higher quality; needs a bit more RAM headroom |
How to run (llama.cpp)
Requires a recent llama.cpp build with Qwen3.5-MoE / qwen35moe support (Feb 2026+; a current release is recommended). On Blackwell GPUs use the CUDA-13.x build.
The optimal consumer configuration keeps all experts on CPU and puts attention/router/shared layers on the GPU:
1# 8 GB VRAM laptop — experts on CPU, rest on GPU
2llama-bench -m ourbox35b-Q3_K_M.gguf -ngl 99 --n-cpu-moe 99 -n 128 -p 512
3
4# interactive
5llama-cli -m ourbox35b-Q3_K_M.gguf -ngl 99 --n-cpu-moe 99 -c 8192 -p "..."
Tip: --n-cpu-moe 99 (all experts on CPU) was measured to be the optimum on an 8 GB card — partially offloading experts to the GPU was slower (per-layer GPU↔CPU transfer overhead outweighs the GPU compute gain).
Objective performance context
How this A3B result compares to running a dense 32–35B (same ~16 GB footprint) — decode is memory-bandwidth bound, so a dense model that reads all its params per token collapses on an 8 GB card, while this A3B stays usable.
Head-to-head A/B, measured by us on the identical laptop (same 8 GB GPU, same engine, same Q3_K_M class, near-identical footprint):
| Model | Active params | Footprint | Decode (same laptop) | Basis |
|---|
| Ourbox-35B (A3B), this repo | ~3 B | 15.6 GiB | 20.01 tok/s | measured |
| Qwen2.5-32B (dense) | 32.8 B | 14.84 GiB | 5.36 tok/s | measured (our A/B) |
→ 3.7× faster from sparsity alone, identical hardware. The only variable is active parameters (3 B vs 32.8 B).
External reference points (for context — not our hardware):
| Setup | Hardware | 35B-class decode | Basis |
|---|
| Dense 32B, best-case 8 GB | RTX 4060 8 GB, short ctx, minimal offload | 10.8 tok/s | published |
| Dense 30B+, forced 8 GB offload | 8 GB consumer GPU | 1–3 tok/s ("impractical") | published guides |
| Dense 32B, fully in VRAM | RTX 3090 / 4090 24 GB desktop (~$700–2,100) | 30–40 tok/s | published |
Takeaways (honest):
- On the identical laptop, we measured a dense 32B (Qwen2.5-32B, ~same footprint) at 5.36 tok/s vs this A3B at 20.01 — a 3.7× speedup attributable purely to A3B sparsity, and ~2× even the best-documented dense-32B result on any 8 GB machine (10.8).
- This lifts a 35B-class model from the field's "impractical on 8 GB" band (1–3 tok/s) to a genuinely usable interactive speed.
- To exceed 20 tok/s on a dense 35B you normally need a 24 GB desktop GPU ($700–2,100). This runs on an 8 GB laptop.
- We do not claim to beat a 24 GB card streaming a model fully in VRAM (those reach 30–40 tok/s dense, and 87–196 tok/s for an A3B held entirely in VRAM). This is explicitly the 8 GB-tier result.
- Numbers labeled estimate are reasoned from public offload-cliff benchmarks, not single measurements.
tg64 is pure token-generation; real chat with long context is lower for every model.
Live demo
Try the identical weights on GPU vs a GPU-less CPU box, live:
Notes
- Text-only. An auxiliary prediction head that the base model carries is omitted in these GGUF files; it is not needed for standard decoding.
- Part of VIDRAFT's efficiency-serving line — the same weights run from a single datacenter GPU down to a consumer laptop.
Learn more
- Running large models on-device without a GPU: Can you run a large LLM without a GPU?
- What model quantization is: What is model quantization?
