FitMyLLM
ByteDance/Dense

BSeed-OSS 36B Instruct

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chatcodingreasoningmultilingualmathtool_use
36B
Parameters
512K
Context length
16
Benchmarks
14
Quantizations
0
Architecture
Dense
Released
2025-08-20
Layers
64
KV Heads
8
Head Dim
128
Family
other

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
18.1 GB
15.1 + 3.0 KV
low
IQ3_XS3.5
19.2 GB
16.2 + 3.0 KV
low
Q3_K_S3.64
19.9 GB
16.9 + 3.0 KV
low
IQ3_M3.76
20.4 GB
17.4 + 3.0 KV
low
Q3_K_M4
21.5 GB
18.5 + 3.0 KV
low
Q3_K_L4.3
22.8 GB
19.8 + 3.0 KV
moderate
IQ4_XS4.46
23.6 GB
20.6 + 3.0 KV
moderate
Q4_K_S4.67
24.5 GB
21.5 + 3.0 KV
moderate
Q4_K_M4.89
25.5 GB
22.5 + 3.0 KV
good
Q5_K_S5.57
28.6 GB
25.6 + 3.0 KV
good
Q5_K_M5.7
29.1 GB
26.1 + 3.0 KV
good
Q6_K6.56
33.0 GB
30.0 + 3.0 KV
excellent
Q8_08.5
41.7 GB
38.7 + 3.0 KV
lossless
FP1616
75.5 GB
72.5 + 3.0 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

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Benchmarks (16)

IFEval85.8
AIME84.7
AA Math84.7
MATH-50084.7
MMLU-PRO81.5
LiveCodeBench76.5
GPQA Diamond72.6
AA Long Context57.7
SWE-bench56.0
τ²-Bench49.4
IFBench41.9
SciCode36.5
AA Intelligence25.2
AA Coding16.7
HLE9.1
Terminal-Bench6.8

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run other:36b-q4_K_M

Tag may need adjustment — check ollama.com/library/other for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for Seed-OSS 36B Instruct

Build Hardware for Seed-OSS 36B Instruct
▸ SPEC SHEET

Seed-OSS 36B Instruct36B Dense.

▸ SPECIFICATIONS
PARAMETERS
36B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
512K tokens
CAPABILITIES
chat, coding, reasoning, multilingual, math, tool_use
RELEASE DATE
2025-08-20
PROVIDER
ByteDance
FAMILY
other
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2515.1 GB82%
IQ3_XS3.516.2 GB84%
Q3_K_S3.6416.9 GB85%
IQ3_M3.7617.4 GB86%
Q3_K_M418.5 GB88%
Q3_K_L4.319.8 GB90%
IQ4_XS4.4620.6 GB92%
Q4_K_S4.6721.5 GB93%
Q4_K_M4.8922.5 GB94%
Q5_K_S5.5725.6 GB96%
Q5_K_M5.726.1 GB96%
Q6_K6.5630.0 GB97%
Q8_08.538.7 GB100%
FP161672.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO81.5
IFEval85.8
GPQA Diamond72.6
LiveCodeBench76.5
AIME84.7
HLE9.1
AA Intelligence25.2
AA Coding16.7
AA Math84.7
aa_ifbench41.9
aa_terminal_bench6.8
aa_tau249.4
aa_scicode36.5
aa_lcr57.7
SWE-bench56.0
MATH-50084.7
§ 03COMPATIBLE GPUs
30 @ Q4_K_M