StepFunStep 3Step3p5ForCausalLM

Step 3.5 Flash — Hardware Requirements & GPU Compatibility

ChatFunctions

Step 3.5 Flash is an efficient mixture-of-experts model from StepFun AI, a Chinese AI startup, featuring roughly 199 billion total parameters. The Flash designation signals its focus on speed and low-latency inference, making it well-suited for interactive applications despite its large total parameter count. Running it locally requires a multi-GPU setup, but its MoE architecture means only a portion of the model activates per token, delivering strong multilingual performance with better throughput than a comparably sized dense model.

199.4B params · MoE11B active262K contextApache 2.0Feb 2026220.3K downloads847 likes17.0K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

Step 3.5 Flash runs on a 192 GB+ Mac or a datacenter GPU

Best local pick → Mac Pro M2 Ultra (192 GB) · Q4_K_M · 137.9 GB · ~46 tok/s

Macs from 96 GB (IQ3_XXS · ~58 tok/s)

Apache 2.0

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Step 3.5 Flash

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Ways to runOther ways

each opens step-by-step

Run in cloud

Your own private copy · Q4_K_M · 96 GB GPU

The easiest way to try this model
Running in a few minutes, easy setup
Chat right here or connect any app

$5+ bonus for new users

Run Step 3.5 Flash

137.9 GB VRAM at 16K context · runs on one H200 NVL 141 GB

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/bartowski/stepfun-ai_Step-3.5-Flash-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/bartowski/stepfun-ai_Step-3.5-Flash-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/bartowski/stepfun-ai_Step-3.5-Flash-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf bartowski/stepfun-ai_Step-3.5-Flash-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/bartowski/stepfun-ai_Step-3.5-Flash-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf bartowski/stepfun-ai_Step-3.5-Flash-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1. Splits the model over 8 GPUs of one server (tensor parallel).
$vllm serve stepfun-ai/Step-3.5-Flash --max-model-len 16384 --tensor-parallel-size 8
Runs the original weights, not the Q4_K_M GGUF; needs ~439 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model mlx-community/Step-3.5-Flash-4bit
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model mlx-community/Step-3.5-Flash-4bit

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$bartowski/stepfun-ai_Step-3.5-Flash-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
B200 (192 GB)CheapestCheapest54 GB free	~258 tok/s	$6.43	Deploy on RunPodfrom $5.98/hr (opens in new tab)	192 GB · 54 GB free · ~258 tok/s · $6.43 / 1M tokens
B300 (288 GB)150 GB free	~258 tok/s	$7.46	Deploy on RunPodfrom $6.94/hr (opens in new tab)	288 GB · 150 GB free · ~258 tok/s · $7.46 / 1M tokens
B200 (192 GB)54 GB free	~258 tok/s	$8.07	Deploy on Vast.aifrom $7.50/hr (opens in new tab)	192 GB · 54 GB free · ~258 tok/s · $8.07 / 1M tokens

Compare GPUs →

Your own private copy of Step 3.5 Flash · Q4_K_M · OpenAI-compatible API

The pod’s first start downloads ~120 GB, billed while it loads.

Open the pod on RunPod
Deploy on RunPodRTX PRO 6000 · $2.49/hr (opens in new tab)
New to RunPod? Sign up and add credit first. If that GPU is gone, pick another with at least 96 GB.
2
Set the model and your key
Press Set overrides → Environment variables. The first variable is already there; set its value:
Key
LLAMA_ARG_HF_REPO
Value
bartowski/stepfun-ai_Step-3.5-Flash-GGUF:Q4_K_M
Press Add Environment variable for your key:
Key
LLAMA_API_KEY
Value
Make up your own key
A long password works. You’ll paste it below as the API key. Without it, anyone who finds the pod’s address can use it.Also set Container disk to 140 GB.Then Set overrides.
Deploy
Press Deploy Pod. Usually ready in 15–60 min.

Use it

RunPod → your pod → Connect → HTTP 8080, copy the link. The key is the one you made up in step 2.

Endpoint URL

API key

Chats and keys stay only in this browser.

https://<POD_ID>-8080.proxy.runpod.net/v1

RunPod → your pod → Connect → HTTP 8080 gives this address. The API key is the one from step 2.

from openai import OpenAI
client = OpenAI(base_url="https://<POD_ID>-8080.proxy.runpod.net/v1", api_key="YOUR_KEY")
r = client.chat.completions.create(
    model="any",
    messages=[{"role": "user", "content": "Hello!"}])
print(r.choices[0].message.content)

Works with any OpenAI client.

https://<POD_ID>-8080.proxy.runpod.net/v1

RunPod → your pod → Connect → HTTP 8080 gives this address. The API key is the one from step 2.

Free on Windows, macOS, Linux, iOS, Android and the web.

Open the web app ↗ (opens in a new tab)Download ↗ (opens in a new tab)

1. Settings → Model Provider → Add → Add Custom Provider
2. API Mode OpenAI API Compatible; paste the API key and the base URL as API Host
3. Model → Fetch, pick the model
4. New chat → pick the provider → start chatting

Free and open source for Windows, macOS and Linux.

Download Jan ↗ (opens in a new tab)

1.Settings → next to Model Providers press +
2.API format OpenAI-compatible; Base URL is the base URL above (with /v1); API key is your key; Create
3.If no model appears, click + in Models and type any
4.New chat → pick the model → start chatting

How Much VRAM Does Step 3.5 Flash Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	Runs on
60.3 GB	73.8 GB	117.1 GB	48 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Runs on RTX PRO 5000 Blackwell 72 GB
65.8 GB	79.3 GB	122.6 GB	55.2 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Runs on H100 SXM 80 GB
68.5 GB	82.1 GB	125.4 GB	55.5 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Runs on H100 SXM 80 GB
74.0 GB	87.6 GB	130.9 GB	62.9 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB
85.0 GB	98.5 GB	141.8 GB	78.2 GB	3-bit · i-quant — Importance-weighted 3-bit	Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB
87.7 GB	101.3 GB	144.6 GB	65.9 GB	2-bit · small K-quant — 2-bit small K-quant	Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB
90.5 GB	104 GB	147.3 GB	81 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Runs on Instinct MI250X 128 GB
93.2 GB	106.7 GB	150 GB	85 GB	3-bit · i-quant small — Importance-weighted 3-bit, small	Runs on Instinct MI250X 128 GB
93.2 GB	106.7 GB	150 GB	69.6 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Runs on Instinct MI250X 128 GB
96.0 GB	109.5 GB	152.8 GB	86 GB	3-bit small — 3-bit small quantization	Runs on Instinct MI250X 128 GB
98.7 GB	112.2 GB	155.5 GB	90.2 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Runs on Instinct MI250X 128 GB
106.9 GB	120.5 GB	163.8 GB	90.2 GB	3-bit medium — 3-bit medium quantization	Runs on Instinct MI250X 128 GB
109.7 GB	123.2 GB	166.5 GB	113.4 GB	4-bit · legacy — 4-bit legacy quantization	Runs on Instinct MI250X 128 GB
112.4 GB	125.9 GB	169.2 GB	93.7 GB	3-bit large — 3-bit large quantization	Runs on Instinct MI250X 128 GB
117.9 GB	131.4 GB	174.7 GB	105.6 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Runs on Instinct MI250X 128 GB
123.4 GB	136.9 GB	180.2 GB	123.6 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Runs on H200 NVL 141 GB
123.4 GB	136.9 GB	180.2 GB	115.6 GB	4-bit small — 4-bit small quantization	Runs on H200 NVL 141 GB
123.4 GB	136.9 GB	180.2 GB	111.6 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Runs on H200 NVL 141 GB
131.6 GB	145.1 GB	188.4 GB	119.9 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Runs on H200 NVL 141 GB
134.3 GB	147.9 GB	191.2 GB	120.3 GB	4-bit large — 4-bit large quantization	Runs on H200 NVL 141 GB
150.8 GB	164.3 GB	207.6 GB	135.9 GB	5-bit small — 5-bit small quantization	Runs on B200 192 GB
156.3 GB	169.8 GB	213.1 GB	140.2 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Runs on B200 192 GB
159.0 GB	172.5 GB	215.8 GB	140.5 GB	5-bit large — 5-bit large quantization	Runs on B200 192 GB
180.9 GB	194.5 GB	237.8 GB	161.9 GB	6-bit, very good — 6-bit quantization, very good quality	Runs on B200 192 GB
219.3 GB	232.9 GB	276.2 GB	209.4 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Runs on Instinct MI325X 256 GB
438.6 GB	452.2 GB	495.5 GB	~398.8 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Runs on 7× H100 SXM 80 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

137.9GBat 16K context

Weights: 130.4 GB
KV cache: 7.2 GB
Overhead: 0.3 GB

Server-class: ~137.9 GB at 16K context · one H200 NVL 141 GB

Or .

Which Hardware Can Run Step 3.5 Flash?

Q4_K_M · 131.6 GB

Step 3.5 Flash (Q4_K_M) requires 131.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 172+ GB is recommended. No consumer GPU has enough memory. 4 devices with unified memory can run Step 3.5 Flash, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (192 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 66.6 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

How good is Step 3.5 Flash?

llmrun Score blends public benchmark boards into one number; 100 = the average open model.

Not enough data yet for an llmrun Score.

A Score needs results on enough independent boards. How the Composite Score is built

Benchmarks

LMArena Text
LMArena · Oct 2026Arena
1393.0
#56/188 open
Arena

Step 3.5 Flash benchmark results

Alternatives to Step 3.5 Flash

Step 3 family · 6 models →

Fine-tunes & variants(1)community builds of Step 3.5 Flash

Step 3.5 Flash REAP 121B A11B

Specifications

Publisher: StepFun
Family: Step 3
Parameters: 199.4B
Active parameters: 11B per token
Architecture: Step3p5ForCausalLM
Context Length: 262,144 tokens
Vocabulary Size: 128,896
Release Date: 2026-02-01
License: Apache 2.0

Resources

Frequently Asked Questions

How much VRAM does Step 3.5 Flash need?

Step 3.5 Flash requires 131.6 GB of VRAM at Q4_K_M, or 438.6 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 199.4B × 4.8 bits ÷ 8 = 119.6 GB

KV Cache + Overhead ≈ 12 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.

VRAM usage by quantization

Q4_K_M

131.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Step 3.5 Flash?

No — Step 3.5 Flash requires at least 60.3 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Step 3.5 Flash?

For Step 3.5 Flash, Q4_K_M (131.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (134.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 60.3 GB.

VRAM requirement by quantization

IQ2_XXS

60.3 GB~53%

IQ3_XS

90.5 GB~73%

Q3_K_L

112.4 GB~86%

Q4_K_M ★

131.6 GB~89%

Q4_K_L

134.3 GB~90%

BF16

438.6 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Step 3.5 Flash on a Mac?

Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run Step 3.5 Flash, the smallest at IQ3_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 131.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (192 GB).

Can I run Step 3.5 Flash locally?

Yes — Step 3.5 Flash can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Step 3.5 Flash?

At Q4_K_M, Step 3.5 Flash can reach ~95 tok/s on AMD Instinct MI350X. 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 → 7.3 GB active ÷ (8000 × 0.65) = 1.40 ms, plus 45 layers × 0.055 ms = 2.48 ms, so 1000 ÷ 3.88 ms = ~258 tok/s

Estimated speed at Q4_K_M (131.6 GB)

NVIDIA B200

~258 tok/s

NVIDIA B300

~258 tok/s

NVIDIA GH200 Grace Hopper Superchip

~210 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Step 3.5 Flash?

At Q4_K_M, the download is about 119.63 GB. The full-precision BF16 version is 398.77 GB. The smallest option (IQ2_XXS) is 54.83 GB.

Which GPUs can run Step 3.5 Flash?

No single consumer GPU has enough VRAM to run Step 3.5 Flash at Q4_K_M (131.6 GB). Multi-GPU or professional hardware is required.

Which devices can run Step 3.5 Flash?

4 devices with unified memory can run Step 3.5 Flash at Q4_K_M (131.6 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.