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Step-3.7-Flash-GGUF

stepfun-ai / Step-3.7-Flash-GGUF

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Model's Last Updated: June 03 2026
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Introduction of Step-3.7-Flash-GGUF

Model Details of Step-3.7-Flash-GGUF

[ModelPage] : https://static.stepfun.com/blog/step-3.7-flash/

1. Introduction

GGUF quantizations of stepfun-ai/Step-3.7-Flash .

Step-3.7-Flash is a 198B-parameter sparse Mixture-of-Experts vision-language model from StepFun-ai, activating ~11B parameters per token for up to 400 t/s throughput. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding, supports a 256K context window, and offers three selectable reasoning levels (low / medium / high) to balance speed, cost, and depth. Built for agentic workloads — tool calling, multi-step reasoning, code, and math — with native multilingual coverage.

A separate mmproj projector ships alongside the language quants for multimodal inference. With 128 GB of unified memory (Mac Studio, DGX Spark, Ryzen AI Max+ 395, etc.), you can privately host Step-3.7-Flash: Q4 quants and below run at full 256K context with high precision.

2. Files
File Quant Size Notes
Step-3.7-flash-BF16.gguf BF16 394 GB Full-precision reference.
Step-3.7-flash-Q8_0.gguf Q8_0 209 GB Near-lossless. Does not use imatrix.
Step-3.7-flash-Q4_K_S.gguf Q4_K_S 112 GB imatrix-calibrated. Balanced quality / size.
Step-3.7-flash-IQ4_XS.gguf IQ4_XS 105 GB imatrix-calibrated. Slightly smaller than Q4_K_S, comparable quality.
Step-3.7-flash-Q3_K_L.gguf Q3_K_L 103 GB imatrix-calibrated. Aggressive size reduction.
Step-3.7-flash-Q3_K_M.gguf Q3_K_M 94 GB imatrix-calibrated. Use when you need to fit on a single 64-96 GB device; expect modest quality loss at low bit-widths.
mmproj-Step-3.7-flash-f16.gguf F16 4 GB Vision projector. Pair with any of the language quants above for image input.
3. Quickstart

Build llama.cpp and run: 

# 1. Clone and build
git clone https://github.com/stepfun-ai/llama.cpp.git
cd llama.cpp
git checkout -b step3.7 origin/step3.7
cmake -B build -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON
cmake --build build --config Release -j$(nproc)

# 2. Test performance (benchmark)
./build/bin/llama-batched-bench \
  -m Step-3.7-flash-Q4_K_S.gguf \
  -c 32768 -b 2048 -ub 2048 \
  -npp 0,2048,8192,16384,32768 -ntg 128 -npl 1

# 3. Text-only inference
./build/bin/llama-cli \
  -m Step-3.7-flash-Q4_K_S.gguf \
  -c 32768 -ngl 99 -fa on \
  -p "Write a Python function to compute the n-th Fibonacci number."

# 4. With vision (image + text)
./build/bin/llama-mtmd-cli \
  -m Step-3.7-flash-Q4_K_S.gguf \
  --mmproj mmproj-Step-3.7-flash-f16.gguf \
  -c 32768 -ngl 99 -fa on \
  --image path/to/image.jpg \
  -p "Describe this image."

# 5. OpenAI-compatible server (text + vision)
./build/bin/llama-server \
  -m Step-3.7-flash-Q4_K_S.gguf \
  --mmproj mmproj-Step-3.7-flash-f16.gguf \
  -c 32768 -ngl 99 -fa on \
  --host 0.0.0.0 --port 8080

For full CLI / server options, see the llama.cpp README .

4. Performance
Apple Mac Studio (M4 max, 128 GB unified memory)

Step-3.7-flash-Q4_K_S 

./llama-batched-bench -m Step-3.7-flash-Q4_K_S.gguf -c 262150 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
PP TG PL N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 2.500 51.20 2.500 51.20
2048 128 1 2176 4.873 420.28 2.639 48.51 7.512 289.68
8192 128 1 8320 20.292 403.70 2.757 46.43 23.049 360.97
16384 128 1 16512 42.854 382.32 2.924 43.77 45.779 360.69
32768 128 1 32896 95.168 344.32 3.223 39.72 98.391 334.34
65536 128 1 65664 233.885 280.21 3.909 32.74 237.794 276.14
131072 128 1 131200 635.499 206.25 5.759 22.23 641.258 204.60
262144 128 1 262272 2362.488 110.96 13.188 9.71 2375.677 110.40

Step-3.7-flash-IQ4_XS 

./llama-batched-bench -m Step-3.7-flash-IQ4_XS.gguf -c 262150 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
PP TG PL N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 2.582 49.58 2.582 49.58
2048 128 1 2176 4.835 423.56 2.679 47.78 7.514 289.60
8192 128 1 8320 19.954 410.55 2.803 45.66 22.757 365.60
16384 128 1 16512 42.142 388.78 2.957 43.29 45.098 366.13
32768 128 1 32896 93.489 350.50 3.288 38.93 96.777 339.91
65536 128 1 65664 227.088 288.59 3.945 32.44 231.033 284.22
131072 128 1 131200 635.047 206.40 5.791 22.10 640.838 204.73
262144 128 1 262272 2170.271 120.79 13.070 9.79 2183.342 120.12

Step-3.7-flash-Q3_K_L 

./llama-batched-bench -m Step-3.7-flash-Q3_K_L.gguf -c 262272 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 3.590 35.66 3.590 35.66
2048 128 1 2176 5.263 389.15 3.702 34.57 8.965 242.72
8192 128 1 8320 21.789 375.97 3.817 33.53 25.606 324.92
16384 128 1 16512 45.819 357.58 3.977 32.18 49.796 331.59
32768 128 1 32896 100.827 324.99 4.308 29.71 105.135 312.89
65536 128 1 65664 242.172 270.62 4.977 25.72 247.149 265.69
131072 128 1 131200 659.645 198.70 6.764 18.92 666.409 196.88
262144 128 1 262272 2200.370 119.14 14.008 9.14 2214.378 118.44
NVIDIA DGX Spark (GB10, 128 GB unified memory)

Step-3.7-flash-Q4_K_S 

./llama-batched-bench -m Step-3.7-flash-Q4_K_S.gguf -c 131300 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072 -ntg 128 -npl 1
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 5.157 24.82 5.157 24.82
2048 128 1 2176 8.021 255.33 4.907 26.08 12.929 168.31
8192 128 1 8320 10.866 753.89 5.169 24.76 16.035 518.86
16384 128 1 16512 29.389 557.49 6.215 20.60 35.603 463.78
32768 128 1 32896 52.501 624.14 6.931 18.47 59.432 553.50
65536 128 1 65664 112.321 583.47 7.769 16.48 120.090 546.79
131072 128 1 131200 281.479 465.66 9.834 13.02 291.313 450.37

Step-3.7-flash-IQ4_XS 

./llama-batched-bench -m Step-3.7-flash-IQ4_XS.gguf -c 262272 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
PP TG PL N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 5.368 23.85 5.368 23.85
2048 128 1 2176 4.250 481.87 5.311 24.10 9.561 227.58
8192 128 1 8320 12.531 653.73 5.817 22.01 18.348 453.46
16384 128 1 16512 24.474 669.44 5.915 21.64 30.389 543.35
32768 128 1 32896 51.976 630.44 6.531 19.60 58.508 562.25
65536 128 1 65664 116.305 563.48 7.934 16.13 124.239 528.53
131072 128 1 131200 298.746 438.74 10.263 12.47 309.009 424.58
262144 128 1 262272 924.872 283.44 14.862 8.61 939.734 279.09

Step-3.7-flash-Q3_K_L 

./llama-batched-bench -m Step-3.7-flash-Q3_K_L.gguf -c 262272 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
PP TG PL N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 5.947 21.52 5.947 21.52
2048 128 1 2176 4.145 494.08 5.623 22.76 9.768 222.77
8192 128 1 8320 14.889 550.20 5.799 22.07 20.688 402.17
16384 128 1 16512 29.374 557.78 6.140 20.85 35.513 464.95
32768 128 1 32896 54.957 596.25 6.744 18.98 61.702 533.15
65536 128 1 65664 129.827 504.79 8.347 15.33 138.174 475.23
131072 128 1 131200 315.402 415.57 10.780 11.87 326.182 402.23
262144 128 1 262272 910.215 288.00 15.568 8.22 925.783 283.30
AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified memory)

Step-3.7-flash-Q4_K_S 

llama-batched-bench.exe -m Step-3.7-flash-Q4_K_S.gguf -c 65664 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536 -ntg 128 -npl 1
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 4.878 26.24 4.878 26.24
2048 128 1 2176 9.367 218.63 5.134 24.93 14.501 150.06
8192 128 1 8320 43.540 188.15 5.508 23.24 49.048 169.63
16384 128 1 16512 111.814 146.53 5.947 21.53 117.761 140.22
32768 128 1 32896 357.819 91.58 6.779 18.88 364.598 90.23
65536 128 1 65664 1342.501 48.82 8.495 15.07 1350.996 48.60

Step-3.7-flash-IQ4_XS 

./llama-batched-bench -m Step-3.7-flash-IQ4_XS.gguf -c 65664 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536 -ntg 128 -npl 1
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 5.931 21.58 5.931 21.58
2048 128 1 2176 8.143 251.50 6.194 20.67 14.337 151.78
8192 128 1 8320 39.899 205.32 6.521 19.63 46.420 179.23
16384 128 1 16512 105.098 155.89 6.891 18.57 111.989 147.44
32768 128 1 32896 338.645 96.76 7.793 16.42 346.439 94.95
65536 128 1 65664 1310.820 50.00 9.489 13.49 1320.309 49.73

Step-3.7-flash-Q3_K_L 

./llama-batched-bench -m Step-3.7-flash-Q3_K_L.gguf -c 262272 -b 2048 -ub 1024 -ctk q8_0 -ctv q8_0 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
0 128 1 128 0.000 0.00 5.015 25.53 5.015 25.53
2048 128 1 2176 10.246 199.88 5.073 25.23 15.319 142.04
8192 128 1 8320 37.229 220.05 5.341 23.96 42.570 195.44
16384 128 1 16512 79.234 206.78 5.489 23.32 84.723 194.89
32768 128 1 32896 179.697 182.35 5.810 22.03 185.507 177.33
65536 128 1 65664 436.593 150.11 6.577 19.46 443.169 148.17
131072 128 1 131200 1262.377 103.83 9.124 14.03 1271.501 103.19
262144 128 1 262272 3487.921 75.16 11.391 11.24 3499.312 74.95
5. Acknowledgments

This release stands on the work of the following authors and communities:

6. License

The GGUF quantization files in this repository are derivative works of stepfun-ai/Step-3.7-Flash and are released under the same Apache 2.0 license.

Component License
Base model weights ( stepfun-ai/Step-3.7-Flash ) Apache-2.0
Calibration dataset ( eaddario/imatrix-calibration ) MIT
Calibration dataset ( NousResearch/hermes-function-calling-v1 ) Apache-2.0
Quantization toolchain ( llama.cpp ) MIT

All calibration datasets retain their original licenses and are used strictly for quantization calibration purposes only.

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