I've been working on getting the 95.5 GiB Qwen3.8-Flash-Next model to run fast on a single 64GB Mac. In my earlier post, I shared a custom expert-streaming fork of llama.cpp . It worked, but decode capped out around ~23–27 tok/s and slowed down as context grew.

Today I'm releasing Slipstream: a compiled C++ Metal inference engine with native SSD expert streaming and speculative drafting for Apple Silicon.

The main result: If you already downloaded my original V3 model (34k+ downloads), you don't need to re-download anything. You can run that exact checkpoint on Slipstream for a 1.76x speedup: 41–52 tok/s (up from 23.1 tok/s in llama.cpp) on the same 64GB Mac.

Even better: decode speed doesn't collapse at long context. Across 3,086 live requests in real coding sessions, it stays flat at 33–44 tok/s all the way out to 130,000 tokens.

Previous posts for context:

• Qwen3.8-Flash-Next in llama.cpp on r/Qwen_AI
• Splash Engine native 8-bit on r/LocalLLaMA

Open source resources:

• Slipstream Engine on GitHub
• Original V3 Model on Hugging Face
• Optional Swift KV-Sparse Model on Hugging Face

1. How to run your existing V3 model on Slipstream

If you have the model from the last post (~/models/qwen38-flash-next-v3), you can point Slipstream directly at it.

Step 1: Clone & build (under 1 minute)

git clone https://github.com/npanj/slipstream.git
cd slipstream
make -j4

Step 2: Download the model (if you don't already have it)

# Downloads the 3 GGUF shards + MTP draft head (~95.5 GiB total)
huggingface-cli download nitinpanj/qwen38-flash-next-v3 \
    --local-dir ~/models/qwen38-flash-next-v3

Step 3: Raise wired GPU memory limit & serve

# Raise wired GPU memory limit once per boot (required on 64 GB Macs):
sudo sysctl iogpu.wired_limit_mb=59392

# Serve your existing model:
./slipstream serve --model ~/models/qwen38-flash-next-v3 --port 8090

>First Run Note: On first launch, Slipstream detects the multi-shard GGUF files and prepares optimized streaming package files into <model>/prepared/ (~5–7 minutes). Subsequent launches load in ~10–15 seconds.

The server exposes a standard OpenAI-compatible API (http://127.0.0.1:8090/v1/chat/completions) ready for curl, Oh My Pi (omp), Claude Code, or OpenCode.

2. Speed: llama.cpp Fork vs. Slipstream (Same V3 Checkpoint)

Here is a direct head-to-head comparison running the exact same 95.5 GiB model files across 6 reasoning and coding tasks on the same M5 Pro (64 GB unified memory, temperature 0.0):

Domain / Task	Prompt Task	llama.cpp Fork	Slipstream	Speedup	llama.cpp TTFT	Slipstream TTFT
Math Reasoning	GSM8K (eggs problem)	24.0 tok/s	43.6 tok/s	1.82x	4,024 ms	2,337 ms
Math Derivation	MATH-500 series ($p - q$)	24.3 tok/s	43.1 tok/s	1.77x	1,655 ms	1,587 ms
Constraint Logic	3-chair deduction	25.4 tok/s	46.0 tok/s	1.81x	1,469 ms	1,042 ms
Python Coding	merge_intervals ($O(N log N)$)	19.7 tok/s	35.0 tok/s	1.77x	1,507 ms	1,070 ms
Systems Coding	Rust CSV parser	22.7 tok/s	37.5 tok/s	1.65x	1,257 ms	859 ms
Tech Writing	Multi-head attention	22.5 tok/s	39.4 tok/s	1.75x	1,267 ms	843 ms
AVERAGE	Across all 6 tasks	23.1 tok/s	40.8 tok/s	1.76x	1,863 ms	1,290 ms

https://preview.redd.it/vh1danbnzwsh1.png?width=3000&format=png&auto=webp&s=aabbd3bcc8611f108d474c1a1b3ae81085c0d9c8

What made Slipstream faster:

1. Asynchronous layer-ahead prefetch (fcntl(F_RDADVISE)): In llama.cpp, synchronous page reads for missed expert matrices stalled the GPU on NVMe latency (~475 ms per chunk). In Slipstream, non-blocking read-ahead hints stream upcoming expert layers from SSD into RAM while the GPU is still executing the previous layer, cutting prefill staging latency by 28%.
2. Hybrid MTP + Prompt Lookup speculation: During tool calls and code generation, Prompt Lookup Decoding (PLD) matches prompt anchors in under 50 ns with 0 allocations, preventing draft rejections. This lifted tool-calling decode from 5.6 tok/s to over 45 tok/s.
3. Metal GPU-mapped n-gram tables: llama.cpp faulted on the 26.8 GiB n-gram table during prefill. Slipstream maps and gathers n-gram embeddings directly in Metal kernels.

3. Context Scaling: Real Telemetry up to 130,000 Tokens

On standard Transformers, decode slows down sharply as context grows because the KV cache swells and memory bandwidth saturates.

Qwen3.8-Flash-Next avoids that through its hybrid architecture:

• 48 recurrent linear DeltaNet layers (fixed $128 \times 128$ hidden state, $O(1)$ memory growth with context).
• Only 16 full-attention layers.

Here is actual telemetry collected across 3,086 live requests during real agent coding sessions on my M5 Pro (64 GB):

Context Range (Tokens)	Live Runs	Average Decode	Median (p50)	Peak Decode	Average TTFT	Notes
< 1,000	314	41.5 tok/s	41.9 tok/s	59.8 tok/s	2.16 s	Short baseline
1k – 4,000	21	41.0 tok/s	42.5 tok/s	64.5 tok/s	5.26 s	Small documents
4k – 8,000	58	43.6 tok/s	43.2 tok/s	67.2 tok/s	7.36 s	Code review turns
8k – 16,000	117	43.6 tok/s	44.6 tok/s	58.2 tok/s	7.91 s	Multi-file context
16k – 32,000	562	38.2 tok/s	40.9 tok/s	58.0 tok/s	13.59 s	Deep agent session
32k – 64,000	1,029	35.0 tok/s	37.5 tok/s	55.6 tok/s	13.24 s	Large repo refactor
64k – 96,000	650	32.4 tok/s	34.7 tok/s	53.9 tok/s	12.81 s	Multi-turn transcript
96k – 130,000	364	32.9 tok/s	33.3 tok/s	43.8 tok/s	7.95 s	Cache-hit deep turns

https://preview.redd.it/qruz5nmpzwsh1.png?width=3300&format=png&auto=webp&s=032c1cb918dc548599e9f15ecd72b66d46828863

Takeaway: Decode speed stays between 33 and 44 tok/s all the way out to 130k tokens. Even at 130k context, it generates tokens faster than stock llama.cpp did on a 500-token prompt.

4. Optional: Swift KV-Sparse Model Variant

If you want higher reasoning accuracy and lower KV cache memory, I also put together an optional Swift variant of this model: Swift-Qwen3.8-Flash-Next-V3.

What Swift changes:

• KV-Sparse Attention: Replaces standard dense attention with KV-sparse layers distilled from Swift-1.5, cutting down RAM pressure at long contexts.
• Spliced Q8 Donor Backbones: Slices 686 high-precision Q8 donor tensors into resident backbone layers for sharper representations.
• Concise Reasoning: Distilled to eliminate repetitive thinking loops in deep contexts.

Both models run on Slipstream using the exact same engine command. Here is how they compare across 145 paired evaluation problems (temperature 0.0, seed 1234):

Domain / Benchmark	Items	Original Flash-Next V3	Swift-Flash-Next V3	Accuracy Delta	Original Decode	Swift Decode
AIME 2025	20	45.0% (9/20)	45.0% (9/20)	0.0%	44.3 tok/s	44.3 tok/s
MATH-500 (L4–5)	35	60.0% (21/35)	62.9% (22/35)	+2.9%	44.8 tok/s	44.8 tok/s
GPQA Diamond	35	45.7% (16/35)	54.3% (19/35)	+8.6%	44.8 tok/s	44.8 tok/s
GSM8K	25	96.0% (24/25)	96.0% (24/25)	0.0%	45.6 tok/s	45.6 tok/s
HumanEval	25	92.0% (23/25)	92.0% (23/25)	0.0%	40.6 tok/s	40.6 tok/s
Hard Systems Logic	5	100.0% (5/5)	100.0% (5/5)	0.0%	39.2 tok/s	39.2 tok/s
OVERALL	145	67.6% (98/145)	70.3% (102/145)	+2.8%	43.9 tok/s	44.4 tok/s

https://preview.redd.it/gt1edfvrzwsh1.png?width=3000&format=png&auto=webp&s=d42ce576fb52c2067a3422bafa594058444cf714

To run the Swift model instead:

huggingface-cli download nitinpanj/Swift-Qwen3.8-Flash-Next-Q4_0-Q8out-v3-GGUF \
    --local-dir ~/models/swift-qwen38-flash-next-v3

./slipstream serve --model ~/models/swift-qwen38-flash-next-v3 --port 8090

5. Foundation for Qwen4

The core primitives in Slipstream:

• 512-route sparse MoE streaming with SSD prefetch
• QSA (Quasi-Sparse Attention) indexer & selection kernels
• Hyper-connection mixing and per-layer embedding gathers
• Metal GPU-mapped n-gram table gathers
• Single-lane speculative verification with PLD & MTP

...were built around this hybrid architecture. If Qwen4 adopts a similar blueprint (hybrid linear recurrence + sparse attention + routed MoE experts), Slipstream should be able to run Qwen4 locally on consumer unified memory hardware on day one.

6. Hardware Tested & Porting to NVIDIA / AMD

• Hardware tested: All testing and benchmarking were done on an Apple MacBook Pro (M5 Pro, 64 GB unified memory, 2 TB SSD).
• CUDA / ROCm ports: I don't have access to modern NVIDIA or AMD GPU hardware, so I can't build or test CUDA/ROCm backends myself.
• If you have hardware and want to help port this: If anyone in the community has NVIDIA or AMD hardware and wants to help bring expert streaming and hybrid speculation to Linux/Windows, I'm happy to help collaborate on the port. Feel free to open an issue on the repo or DM me.

7. Credits & Upstream

• Splash Team (Incoai): Full credit to the creators of Splash. Their C++ Metal speculative decoding design and memory architecture provided the foundation for this work. I will prepare a clean PR proposing these Flash-Next and SSD streaming extensions to the Splash upstream repo in case they want to incorporate them.
• ds4 Team: For their insights on Metal router numerical precision (Taylor polynomial softplus expansion) and streaming scheduling designs.
• Qwen Team: For training Qwen3.8-Flash-Next and releasing the hybrid linear MTP architecture.
• ukisai: For the Swift-1.5 distillation work enabling KV-sparse reasoning.
• bartowski & unsloth: For donor quants and quantization tooling.
• mihailescu2m: For the initial expert streaming work in llama.cpp.

In the past few days, I have gained the following experiences:

Although the performance of the Qwen 3.8 27B model is not bad, but...

Let's start at the very beginning. In terms of raw computing performance, coding based on my local measurements and in practice, it seems that the Qwen3.6-27B-Q4_K++ models are almost on the same level as the Qwen3.8 27B model. The big difference is that the 3.8 thinks a lot. This is where its advantage comes into play. The tool calling is better, so it is more efficient overall. However, it is not necessarily smarter.

If I set the thinking value to med or min for the Qwen 3.8 27B instead of xhigh, it consistently underperforms in the tests compared to the Qwen 3.6 27B.

So Qwen 3.8 27B is only smarter if the reasoning level is xhigh, but then it is incredibly slow and generates a lot of tokens. And the worst thing is that even though the KV cache is Q8 quantized, it still requires a brutal amount of RAM/VRAM compared to Qwen 3.6.

And here comes the point that needs to be discussed.

Qwen 3.6 27B runs with 200k+ ctx on my 22 GB GPU in good quality.

Qwen 3.8 27B does not. This is not enough for it, there is a maximum of 100k ctx in the VRAM and the model is only Q4 quality, the KV cache Q8, because below it the model will be brutally stupid, a lobotomy. Obviously the best would be F16.

So I think that for planning and ideation, thinking, Qwen 3.8 27B is suitable, but for implementation 3.6 27B is perfect and MUCH faster. But...

This is where Qwen 3.8 Flash Next comes into play.

While on the 2080 Ti card (22GB Modded) on the i9-7980XE CPU with quad channel DDR4 (128GB) RAM:

the Qwen 3.8 27B model runs at Q4 quantum quality, ctx 100k, KV Q8 settings at approx. 35-45 t/s with MTP,

the Qwen 3.8 Flash Next (Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS) with STRATA, ngram can easily fit on 1 GPU with 256k ctx (KV Q8) and 64 GB RAM is enough for it. The speed is currently 60-75 t/s and the strata is developing at full speed.

A fast SSD is probably also important for this, but I'm not sure, because I think the ngram table is in the RAM now. By the way, I only have Gen3 PCI-e ports, so I solved the SSD acceleration by connecting 2 average SSDs to nvme ports and creating an array with RAID0, and then their speed added up.

Experiences about the Qwen 3.8 FN:

It doesn't overthink things. It can think a lot, but not always, only when justified. It's precise, follows instructions, smart. I'll risk saying that based on my usage habits, it's smarter than the GLM 5.3 Flash.

Since its speed is also better and the VRAM requirement is less than the Qwen 3.8 27B, the question now is which model should I choose? I think the answer is clear.

Do you have similar experiences?