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Qwen3.5-35B-A3B-RAMP-v2-15G
Kevletesteur/Qwen3.5-35B-A3B-RAMP-v2-15G
Qwen3.5-35B-A3B-RAMP-v2-15G is a text generation model from Kevletesteur. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
Hardware-optimized GGUF quantization of Qwen3.5-35B-A3B for RTX 5060 Ti 16GB.
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955
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16 GB
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.gguf16 GB · 100%
At a glance
- Task
- Text Generation
- License
- apache-2.0
- Access
- Public
- Created
- Mar 29, 2026
- Updated
- Apr 7, 2026
- SHA
- 6da52a7f
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Base models
Model card
From the Hugging Face model README
Qwen3.5-35B-A3B — RAMP v2 (15.2 GB)
Hardware-optimized GGUF quantization of Qwen3.5-35B-A3B for RTX 5060 Ti 16GB.
Produced with RAMP (RL-guided Adaptive Mixed-Precision), a data-free quantization pipeline that uses per-tensor sensitivity analysis and evolutionary search to find the optimal mixed-precision configuration for your specific hardware.
Key specs
| Metric | Value |
|---|---|
| Base model | Qwen/Qwen3.5-35B-A3B |
| File size | 15.2 GB |
| Average BPW | 3.78 |
| Base quant type | IQ3_S |
| Critical path overrides | Q8_0 (SSM gates, norms), Q6_K/Q5_K (attention QKV, shared expert) |
| Generation speed | ~80 tok/s on the chimere-server HTTP path (~90 tok/s bare ik_llama-server), RTX 5060 Ti, sm_120, -ngl 99 --n-cpu-moe 4 |
| Context | 32K tokens (q8_0 keys + q4_0 values KV cache) |
| Functional benchmark | 30/30 |
| VRAM usage | ~14 GB (GPU) + CPU experts |
What makes RAMP different
Standard quantization applies the same precision to all tensors. RAMP assigns per-tensor precision based on sensitivity analysis:
- SSM gates and norms → Q8_0 (critical for GDN recurrent state stability)
- Attention Q/K/V projections → Q5_K/Q6_K (quality-sensitive)
- MoE shared expert → Q5_K (always active, high impact)
- MoE routed experts → IQ3_S (256 experts, only 8 active per token)
This is built with a custom imatrix calibrated on French + English + code + clinical (kiné) data, not generic wiki text.
How to use
# With ik_llama.cpp (recommended for sm_120 GPUs)
./llama-server \
-m Qwen3.5-35B-A3B-RAMP-v2-15g.gguf \
-ngl 99 --n-cpu-moe 4 \
-np 1 -c 32768 \
--cache-type-k q8_0 --cache-type-v q4_0
# With stock llama.cpp
./llama-server \
-m Qwen3.5-35B-A3B-RAMP-v2-15g.gguf \
-ngl 99 \
-c 32768
Quantization pipeline
- Start from Qwen3.5-35B-A3B BF16 (Unsloth GGUF)
- Custom imatrix: domain-calibrated (BFCL + MoT + Codeforces + French clinical)
- RAMP sensitivity analysis: per-tensor NSDS scoring (data-free)
llama-quantize --imatrix chimere --custom-qwith 317 tensor overrides- Validation: 30/30 functional bench, perplexity check
Previous versions:
- RAMP v1 (17 GB, Q3_K_M base, no imatrix) — backup
- IQ3_S custom-mix (14.71 GB, 3.56 BPW) — backup
Hardware tested
- GPU: NVIDIA RTX 5060 Ti 16GB (Blackwell, sm_120)
- CPU: Intel i5-14600KF
- RAM: 32GB DDR5
- Driver: 590.48 (CUDA 12.8 toolkit)
Related
- chimere — Rust inference runtime
- ramp-quant — Quantization pipeline source code
- chimere-odo — Inference orchestrator
Author
Kevin Remondiere — Independent ML researcher, Bayonne, France
License
Apache 2.0 (quantization pipeline and model card). The base model follows Qwen's license.
- Task
- Text Generation
- License
- apache-2.0
- Languages
- en, fr, zh
- Created
- Mar 29, 2026
- Updated
- Apr 7, 2026