DeepSeek V4 Flash のための軽量ネイティブ推論エンジン「ds4.c」が GitHub に公開

ds4.c is a small native inference engine for DeepSeek V4 Flash. It is

intentionally narrow: not a generic GGUF runner, not a wrapper around another

runtime, and not a framework. The main path is a DeepSeek V4 Flash-specific

Metal graph executor with DS4-specific loading, prompt rendering, KV state, and

server API glue.

This project would not exist without llama.cpp and GGML, make sure to read

the acknowledgements section, a big thank you to Georgi Gerganov and all the

other contributors.

Now, back at this project. Why we believe DeepSeek v4 Flash to be a pretty special

model deserving a stand alone engine? Because after comparing it with powerful smaller

dense models, we can report that:

• DeepSeek v4 Flash is faster because of less active parameters.

• In thinking mode, if you avoid max thinking, it produces a thinking section that is a lot shorter than other models, even 1/5 of other models in many cases, and crucially, the thinking section length is proportional to the problem complexity. This makes DeepSeek v4 Flash usable with thinking enabled when other models are practically impossible to use in the same conditions.

• The model features a context window of 1 million tokens.

• Being so large, it knows more things if you go sampling at the edge of knowledge. For instance asking about Italian show or political questions soon uncovers that 284B parameters are a lot more than 27B or 35B parameters.

• It writes much better English and Italian. It feels a quasi-frontier model.

• The KV cache is incredibly compressed, allowing long context inference on local computers and on disk KV cache persistence.

• It works well with 2-bit quantization, if quantized in a special way (read later). This allows to run it in MacBooks with 128GB of RAM.

• We expect DeepSeek to release updated versions of v4 Flash in the future, even better than the current one.

That said, a few important things about this project:

• The local inference landscape contains many excellent projects, but new models are released continuously, and the attention immediately gets captured by the next model to implement. This project takes a deliberately narrow bet: one model at a time, official-vector validation (logits obtained with the official implementation), long-context tests, and enough agent integration to know if it really works. The exact model may change as the landscape evolves, but the constraint remains: local inference credible on high end personal machines or Mac Studios, starting from 128GB of memory.

• This software is developed with strong assistance from GPT 5.5 and with humans leading the ideas, testing, and debugging. We say this openly because it shaped how the project was built. If you are not happy with AI-developed code, this software is not for you. The acknowledgement below is equally important: this would not exist without llama.cpp and GGML, largely written by hand.

• This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. The KV cache is actually a first-class disk citizen.

• Our vision is that local inference should be a set of three things working well together, out of the box: A) inference engine with HTTP API + B) GGUF specially crafted to run well under a given engine and given assumptions + C) testing and validation with coding agents implementations. This inference engine only runs with the GGUF files provided. It gets tested against officially obtained logits at different context sizes. This project exists because we wanted to make one local model feel finished end to end, not just runnable. However this is just alpha quality code, so probably we are not still there.

• This is Metal-only, may implement CUDA support in the future? Perhaps, but nothing more. The CPU path is only for correctness check, but warning: current macOS versions have a bug in the virtual memory implementation that will crash the kernel if you try to run the CPU code. Remember? Software sucks. It was not possible to fix the CPU inference to avoid crashing, since each time you have to restart the computer, which is not funny. Help us, if you have the guts.

Acknowledgements to llama.cpp and GGML

ds4.c does not link against GGML, but it **exists thanks to the path opened by the

llama.cpp project and the kernels, quantization formats, GGUF ecosystem, and hard-won

engineering knowledge developed there**.

We are thankful and indebted to llama.cpp

and its contributors. Their implementation, kernels, tests, and design choices were

an essential reference while building this DeepSeek V4 Flash-specific inference path.

Some source-level pieces are retained or adapted here under the MIT license: GGUF

quant layouts and tables, CPU quant/dot logic, and certain Metal kernels. For this

reason, and because we are genuinely grateful, we keep the GGML authors copyright

notice in our LICENSE file.

Model Weights

This implementation only works with the DeepSeek V4 Flash GGUFs published for

this project. It is not a general GGUF loader, and arbitrary DeepSeek/GGUF files

will not have the tensor layout, quantization mix, metadata, or optional MTP

state expected by the engine. The 2 bit quantizations provided here are not

a joke: they behave well, work under coding agents, call tools in a reliable way.

The 2 bit quants use a very asymmetrical quantization: only the routed MoE

experts are quantized, up/gate at IQ2_XXS, down at Q2_K. They are the

majority of all the model space: the other components (shared experts,

projections, routing) are left untouched to guarantee quality.

Download one main model:

./download_model.sh q2   # 128 GB RAM machines
./download_model.sh q4   # >= 256 GB RAM machines

The script downloads from https://huggingface.co/antirez/deepseek-v4-gguf,

stores files under ./gguf/, resumes partial downloads with curl -C -, and

updates ./ds4flash.gguf to point at the selected q2/q4 model. Authentication

is optional for public downloads, but --token TOKEN, HF_TOKEN, or the local

Hugging Face token cache are used when present.

./download_model.sh mtp fetches the optional speculative decoding support

GGUF. It can be used with both q2 and q4, but must be enabled explicitly with

--mtp. The current MTP/speculative decoding path is still experimental: it is

correctness-gated and currently provides at most a slight speedup, not a

meaningful generation-speed win.

Then build:

make

./ds4flash.gguf is the default model path used by both binaries. Pass -m to

select another supported GGUF from ./gguf/. Run ./ds4 --help and

./ds4-server --help for the full flag list.

Speed

These are single-run Metal CLI numbers with --ctx 32768, --nothink, greedy

decoding, and -n 256. The short prompt is a normal small Italian story

prompt. The long prompts exercise chunked prefill plus long-context decode.

Q4 requires the larger-memory machine class, so M3 Max Q4 numbers are N/A.

Machine

Quant

Prompt

Prefill

Generation

MacBook Pro M3 Max, 128 GB

q2

short

58.52 t/s

26.68 t/s

MacBook Pro M3 Max, 128 GB

q2

11709 tokens

250.11 t/s

21.47 t/s

MacBook Pro M3 Max, 128 GB

q4

short

N/A

N/A

MacBook Pro M3 Max, 128 GB

q4

long

N/A

N/A

Mac Studio M3 Ultra, 512 GB

q2

short

84.43 t/s

36.86 t/s

Mac Studio M3 Ultra, 512 GB

q2

11709 tokens

468.03 t/s

27.39 t/s

Mac Studio M3 Ultra, 512 GB

q4

short

78.95 t/s

35.50 t/s

Mac Studio M3 Ultra, 512 GB

q4

12018 tokens

448.82 t/s

26.62 t/s

CLI

One-shot prompt:

./ds4 -p "Explain Redis streams in one paragraph."

No -p starts the interactive prompt:

./ds4
ds4>

The interactive CLI is a real multi-turn DS4 chat. It keeps the rendered chat

transcript and the live Metal KV checkpoint, so each turn extends the previous

conversation. Useful commands are /help, /think, /think-max, /nothink,

/ctx N, /read FILE, and /quit. Ctrl+C interrupts the current generation

and returns to ds4>.

The CLI defaults to thinking mode. Use /nothink or --nothink for direct

answers. --mtp MTP.gguf --mtp-draft 2 enables the optional MTP speculative

path; it is useful only for greedy decoding, currently uses a confidence gate

(--mtp-margin) to avoid slow partial accepts, and should be treated as an

experimental slight-speedup path.

Server

Start a local OpenAI/Anthropic-compatible server:

./ds4-server --ctx 100000 --kv-disk-dir /tmp/ds4-kv --kv-disk-space-mb 8192

The server is Metal-only. It keeps one mutable graph/KV checkpoint in memory,

so stateless clients that resend a longer version of the same prompt can reuse

the shared prefix instead of pre-filling from token zero.

Request parsing and sockets run in client threads, but inference itself is

serialized through one Metal worker. The current server does not batch multiple

independent requests together; concurrent requests wait their turn on the single

live graph/session.

Supported endpoints:

• GET /v1/models

• GET /v1/models/deepseek-v4-flash

• POST /v1/chat/completions

• POST /v1/completions

• POST /v1/messages

/v1/chat/completions accepts the usual OpenAI-style messages,

max_tokens/max_completion_tokens, temperature, top_p, top_k, min_p,

seed, stream, stream_options.include_usage, tools, and tool_choice.

Tool schemas are rendered into DeepSeek's DSML tool format, and generated DSML

tool calls are mapped back to OpenAI tool calls.

/v1/messages is the Anthropic-compatible endpoint used by Claude Code style

clients. It accepts system, messages, tools, tool_choice, max_tokens,

temperature, top_p, top_k, stream, stop_sequences, and thinking

controls. Tool uses are returned as Anthropic tool_use blocks.

Both APIs support SSE streaming. In thinking mode, reasoning is streamed in the

native API shape instead of being mixed into final text.

Minimal OpenAI example:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model":"deepseek-v4-flash",
    "messages":[{"role":"user","content":"List three Redis design principles."}],
    "stream":true
  }'

Agent Client Usage

ds4-server can be used by local coding agents that speak OpenAI-compatible

chat completions. Start the server first, and set the client context limit no

higher than the --ctx value you started the server with:

./ds4-server --ctx 100000 --kv-disk-dir /tmp/ds4-kv --kv-disk-space-mb 8192

You can use larger context and larger cache if you wish. Full context of

1M tokens is going to use more or less 26GB of memory (compressed indexer

alone will be like 22GB), so configure a context which makes sense in

your system. With 128GB of RAM you would run the 2-bit quants, which are

already 81GB, 26GB are going to be likely too much, so a context window

of 100~300k tokens is wiser.

The 384000 output limit below avoids token caps since the model is able

to generate very long replies otherwise (up to 384k tokens). The server

still stops when the configured context window is full.

For opencode, add a provider and agent entry to

~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ds4": {
      "name": "ds4.c (local)",
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://127.0.0.1:8000/v1",
        "apiKey": "dsv4-local"
      },
      "models": {
        "deepseek-v4-flash": {
          "name": "DeepSeek V4 Flash (ds4.c local)",
          "limit": {
            "context": 100000,
            "output": 384000
          }
        }
      }
    }
  },
  "agent": {
    "ds4": {
      "description": "DeepSeek V4 Flash served by local ds4-server",
      "model": "ds4/deepseek-v4-flash",
      "temperature": 0
    }
  }
}

For Pi, add a provider to ~/.pi/agent/models.json:

{
  "providers": {
    "ds4": {
      "name": "ds4.c local",
      "baseUrl": "http://127.0.0.1:8000/v1",
      "api": "openai-completions",
      "apiKey": "dsv4-local",
      "compat": {
        "supportsStore": false,
        "supportsDeveloperRole": false,
        "supportsReasoningEffort": true,
        "supportsUsageInStreaming": true,
        "maxTokensField": "max_tokens",
        "supportsStrictMode": false,
        "thinkingFormat": "deepseek",
        "requiresReasoningContentOnAssistantMessages": true
      },
      "models": [
        {
          "id": "deepseek-v4-flash",
          "name": "DeepSeek V4 Flash (ds4.c local)",
          "reasoning": true,
          "thinkingLevelMap": {
            "off": null,
            "minimal": "low",
            "low": "low",
            "medium": "medium",
            "high": "high",
            "xhigh": "xhigh"
          },
          "input": ["text"],
          "contextWindow": 100000,
          "maxTokens": 384000,
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          }
        }
      ]
    }
  }
}

Optionally make it the default Pi model in ~/.pi/agent/settings.json:

{
  "defaultProvider": "ds4",
  "defaultModel": "deepseek-v4-flash"
}

For Claude Code, use the Anthropic-compatible endpoint. A wrapper like this

matches the local ~/bin/claude-ds4 setup:

#!/bin/sh
unset ANTHROPIC_API_KEY

export ANTHROPIC_BASE_URL="${DS4_ANTHROPIC_BASE_URL:-http://127.0.0.1:8000}"
export ANTHROPIC_AUTH_TOKEN="${DS4_API_KEY:-dsv4-local}"
export ANTHROPIC_MODEL="deepseek-v4-flash"

export ANTHROPIC_CUSTOM_MODEL_OPTION="deepseek-v4-flash"
export ANTHROPIC_CUSTOM_MODEL_OPTION_NAME="DeepSeek V4 Flash local ds4"
export ANTHROPIC_CUSTOM_MODEL_OPTION_DESCRIPTION="ds4.c local GGUF"

export ANTHROPIC_DEFAULT_SONNET_MODEL="deepseek-v4-flash"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="deepseek-v4-flash"
export ANTHROPIC_DEFAULT_OPUS_MODEL="deepseek-v4-flash"
export CLAUDE_CODE_SUBAGENT_MODEL="deepseek-v4-flash"

export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
export CLAUDE_CODE_DISABLE_NONSTREAMING_FALLBACK=1
export CLAUDE_STREAM_IDLE_TIMEOUT_MS=600000

exec "$HOME/.local/bin/claude" "$@"

Claude Code may send a large initial prompt, often around 25k tokens, before it

starts doing useful work. Keep --kv-disk-dir enabled: after the first expensive

prefill, the disk KV cache lets later continuations or restarted sessions reuse

the saved prefix instead of processing the whole prompt again.

Thinking Modes

DeepSeek V4 Flash has distinct non-thinking, thinking, and Think Max modes.

The server defaults to thinking mode. reasoning_effort=max requests Think

Max, but it is only applied when the context size is large enough for the model

card recommendation; smaller contexts fall back to normal thinking. OpenAI

reasoning_effort=xhigh still maps to normal thinking, not Think Max.

For direct replies, use thinking: {"type":"disabled"}, think:false, or a

non-thinking model alias such as deepseek-chat.

Disk KV Cache

Chat/completion APIs are stateless: agent clients usually resend the whole

conversation every request. ds4-server handles this by comparing the rendered

token stream with cached token prefixes. The live in-memory checkpoint covers

the current session; the disk KV cache makes useful prefixes survive session

switches and server restarts.

For RAM reasons there is currently only one live KV cache in memory. When a new

unrelated session replaces it, the old checkpoint can only be resumed without

re-processing if it was written to the disk KV cache. In other words, memory

cache handles the active session; disk cache is the resume mechanism for

different sessions.

Enable it with:

./ds4-server --kv-disk-dir /tmp/ds4-kv --kv-disk-space-mb 8192

The cache key is the SHA1 of exact token IDs, not raw text. Each token ID is

hashed as a little-endian 32-bit integer, and files are named <sha1>.kv.

The file is intentionally written with ordinary read/write I/O, not

mmap, so restoring cache entries does not add more VM mappings to a process

that already maps the model.

Tool calls also keep a small exact-DSML replay map keyed by unguessable tool

IDs, so client JSON history can be rendered back to the exact sampled text. Use

--disable-exact-dsml-tool-replay to disable this and fall back to canonical

JSON-to-DSML rendering.

On disk, a cache file is:

KVC fixed header, 48 bytes
u32 rendered_text_bytes
rendered_text_bytes of UTF-8-ish token text
DS4 session payload, payload_bytes from the KVC header
optional tool-id map section

The fixed header is little-endian:

0   u8[3]  magic = "KVC"
3   u8     version = 1
4   u8     routed expert quant bits, currently 2 or 4
5   u8     save reason: 0 unknown, 1 cold, 2 continued, 3 evict, 4 shutdown
6   u8     extension flags, bit 0 = appended tool-id map
7   u8     reserved
8   u32    cached token count
12  u32    hit count
16  u32    context size the snapshot was written for
20  u8[4]  reserved
24  u64    creation Unix time
32  u64    last-used Unix time
40  u64    DS4 session payload byte count

The rendered text is the tokenizer-decoded text for the cached token prefix.

It is stored only for observability, so humans can inspect a cache directory

without decoding token IDs. It is not used as the key and it is not trusted

when loading; after load, the stored checkpoint tokens must still match the

incoming request prefix.

The optional tool-id map is present only when header extension bit 0 is set.

Appended sections use fixed bit order, so future extension bits can add fields

without ambiguity. The map stores unguessable API tool call IDs back to the

exact DSML block the model sampled. Only mappings whose DSML block is present

in the rendered cached text are stored. This lets restarted servers render

later client history byte-for-byte like the original model output, even if the

client reorders JSON arguments.

The DS4 session payload starts with thirteen little-endian u32 fields:

0   magic = "DSV4"
1   payload version = 1
2   saved context size
3   prefill chunk size
4   raw KV ring capacity
5   raw sliding-window length
6   compressed KV capacity
7   checkpoint token count
8   layer count
9   raw/head KV dimension
10  indexer head dimension
11  vocabulary size
12  live raw rows serialized below

Then it stores:

• u32[token_count] checkpoint token IDs.

• float32[vocab_size] logits for the next token after that checkpoint.

• u32[layer_count] compressed attention row counts.

• u32[layer_count] ratio-4 indexer row counts.

• For every layer: the live raw sliding-window KV rows, written in logical

position order rather than physical ring order.

• For compressed layers: live compressed KV rows and compressor frontier

tensors.

• For ratio-4 compressed layers: live indexer compressed rows and indexer

frontier tensors.

The logits are raw IEEE-754 float32 values from the host ds4_session

buffer. They are saved immediately after the checkpoint tokens so a loaded

snapshot can sample or continue from the exact next-token distribution without

running one extra decode step. MTP draft logits/state are not persisted; after

loading a disk checkpoint the draft state is invalidated and rebuilt by normal

generation.

The tensor payload is DS4-specific KV/session state, not a generic inference

graph dump. It is expected to be portable only across compatible ds4.c

builds for this model layout.

The cache stores checkpoints at four moments:

• cold: after a long first prompt reaches a stable prefix, before generation.

• continued: when prefill or generation advances the live conversation by the configured interval.

• evict: before an unrelated request replaces the live in-memory session.

• shutdown: when the server exits cleanly.

Cold saves intentionally trim a small token suffix and align down to a prefill

chunk boundary. This avoids common BPE boundary retokenization misses when a

future request appends text to the same prompt. The defaults are conservative:

store prefixes of at least 512 tokens, cold-save prompts up to 30000 tokens,

trim 32 tail tokens, and align to 2048-token chunks. The important knobs are:

• --kv-cache-min-tokens

• --kv-cache-cold-max-tokens

• --kv-cache-continued-interval-tokens

• --kv-cache-boundary-trim-tokens

• --kv-cache-boundary-align-tokens

By default, checkpoints may be reused across the 2-bit and 4-bit routed-expert

variants if the token prefix matches. Use --kv-cache-reject-different-quant

when you want strict same-quant reuse only.

The cache directory is disposable. If behavior looks suspicious, stop the

server and remove it. You can investigate what is cached with hexdump as

the kv cache files include the verbatim prompt cached.

Backends

The default backend is Metal:

./ds4 -p "Hello" --metal

There is also a CPU reference/debug path:

./ds4 -p "Hello" --cpu

Do not treat the CPU path as the production target. The server is Metal-only,

and the optimized implementation lives in the Metal graph path. This may

change in the future.

Test Vectors

tests/test-vectors contains short and long-context continuation vectors

captured from the official DeepSeek V4 Flash API. The requests use

deepseek-v4-flash, greedy decoding, thinking disabled, and the maximum

top_logprobs slice exposed by the API. Local vectors are generated with

./ds4 --dump-logprobs and compared by token bytes, so tokenizer/template or

attention regressions show up before they become long generation failures.

All project tests are driven by the C runner:

make test                  # ./ds4_test --all
./ds4_test --logprob-vectors
./ds4_test --server

Debugging Notes

When a generation looks wrong, three small tools are usually enough to get a

first answer:

./ds4 --dump-tokens -p "..."
./ds4 --dump-logprobs /tmp/out.json --logprobs-top-k 20 --temp 0 -p "..."
./ds4-server --trace /tmp/ds4-trace.txt ...

• --dump-tokens tokenizes the -p or --prompt-file string exactly as

written, recognizes DS4 protocol specials, and then exits before inference

starts. For example, the DSML tool close marker starts as two tokens: </

and ｜DSML｜.

• --dump-logprobs stores a greedy continuation with the top local

alternatives at each step, which helps separate sampling choices from

logit/model issues.

• ds4-server --trace writes the rendered prompts, cache decisions, generated

text, and tool-parser events for a whole agent session.