Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap — Hardware Requirements & GPU Compatibility
ChatReasoning
Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap is a 14.1B-parameter open language model from tvall43 in the Qwen 3.5 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 8.87 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q3_K_M · 64K)Best on 24 GB (Q4_K_M · ~182 tok/s · 256K)Macs from 16 GB (Q4_K_M · ~43 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/tvall43/Qwen3.5-14B-A3B-Claude-4.6-Opus-Reasoning-Distilled-reap-ggufQ4_K_M fits fully on the RTX 4090 · 15.0 GB free · 16K context
Download: Qwen3.5-14B-A3B-Claude-4.6-Opus-Reasoning-Distilled-reap-Q4_K_M.gguf8.48 GBtvall43
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (3 GB headroom) · Q4_K_M
- Generation speed
- ~88 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.4 GB | 6.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~219 tok/s | |
| 7.3 GB | 7.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~204 tok/s | |
| 8.9 GB | 9.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~182 tok/s | |
| 10.5 GB | 10.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~164 tok/s | |
| 12.1 GB | 12.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~150 tok/s | |
| 14.5 GB | 14.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~132 tok/s | |
| 28.7 GB | 28.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 28.7 GB |
Selected Q4_K_M
4-bit medium quantization9.0GBat 16K context
- Weights
- 8.5 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 15.1 GB free · ~182 tok/s
≥50 tok/s needs ≥186 GB/s · RTX 5090 ~247, RTX 3090 Ti ~182
Which Hardware Can Run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
Q4_K_M · 8.9 GBQwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap (Q4_K_M) requires 8.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 262K context window can add up to 2.6 GB, bringing total usage to 11.5 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 3.0 GB free · up to 256K ctx
10 GPUs
RTX 3080 Ti~171Simulate output on RTX 3080 TiRTX 5070~140Simulate output on RTX 5070RTX 4070~114Simulate output on RTX 407020 GB
fits · 11.1 GB free · up to 256K ctx
1 GPU
RX 7900 XT~80Simulate output on RX 7900 XT32–40 GB
fits · 23.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~247Simulate output on RTX 5090M5 Max36 GB~68Simulate output on Mac Studio M5 Max (36 GB)M532 GB~36Simulate output on MacBook Air 13" M5 (32 GB)Orin~54Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~35Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap
Qwen 3.5 family · 34 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| JiRackUltra 14BCMSManhattan | — | 12.4 GB | ~69 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
Specifications
- Publisher
- tvall43
- Family
- Qwen 3.5
- Parameters
- 14.1B
- Active parameters
- 3.4B per token
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-09
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap need?
Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap requires 8.9 GB of VRAM at Q4_K_M, or 28.7 GB at BF16. Full 262K context adds up to 2.6 GB (11.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.1B × 4.8 bits ÷ 8 = 8.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 3 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
Yes, at Q8_0 (14.5 GB) or lower. Higher quantizations like BF16 (28.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
For Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap, Q4_K_M (8.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap locally?
Yes — Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
At Q4_K_M, Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap can reach ~118 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~182 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.2 GB active ÷ (8000 × 0.65) = 0.42 ms, plus 40 layers × 0.055 ms = 2.20 ms, so 1000 ÷ 2.62 ms = ~382 tok/s
Estimated speed at Q4_K_M (8.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
At Q4_K_M, the download is about 8.48 GB. The full-precision BF16 version is 28.27 GB. The smallest option (Q2_K) is 6.01 GB.
Which GPUs can run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
40 consumer GPUs can run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap?
150 devices with unified memory can run Qwen3.5 14B A3B Claude 4.6 Opus Reasoning Distilled Reap at Q4_K_M (8.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.