deepseek-ai · text

deepseek-coder-6.7b-instruct

deepseek-ai/deepseek-coder-6.7b-instruct

deepseek-coder-6.7b-instruct at Q4_K_M is exactly 4,083,015,904 bytes (3.80 GiB / 4.08 GB) — an effective 4.846 bits per weight, not the nominal 4. Its KV cache at 32K is 16.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
6.7B
Architecture
llama
32 layers
Context
16,384
native (config.json)
License
other

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
Q2_K2.36 GiB2,534,630,3363.008—second-state
Q2_K2.63 GiB2,827,706,5923.356—TheBloke
Q2_K2.63 GiB2,827,706,5923.356—Dzluck
Q3_K_S2.75 GiB2,950,176,9923.501—Dzluck
Q3_K_S2.75 GiB2,950,176,9923.501—TheBloke
Q3_K_S2.75 GiB2,950,177,7283.501—second-state
Q3_K_M3.07 GiB3,299,877,0883.917—TheBloke
Q3_K_M3.07 GiB3,299,877,0883.917—Dzluck
Q3_K_M3.07 GiB3,299,877,8243.917—second-state
Q3_K_L3.35 GiB3,598,983,3924.271—TheBloke
Q3_K_L3.35 GiB3,598,983,3924.271—Dzluck
Q3_K_L3.35 GiB3,598,984,1284.271—second-state
Q4_03.56 GiB3,827,818,7204.543—TheBloke
Q4_03.56 GiB3,827,818,7204.543—Dzluck
Q4_03.56 GiB3,827,819,4564.543—second-state
Q4_K_S3.59 GiB3,858,751,7124.580—Dzluck
Q4_K_S3.59 GiB3,858,751,7124.580—TheBloke
Q4_K_S3.59 GiB3,858,752,4484.580—second-state
Q4_K_M3.80 GiB4,083,015,9044.846—Dzluck
Q4_K_M3.80 GiB4,083,015,9044.846—TheBloke
Q4_K_M3.80 GiB4,083,016,6404.846—second-state
Q5_04.33 GiB4,653,834,4645.523—TheBloke
Q5_K_S4.33 GiB4,653,834,4645.523—TheBloke
Q5_04.33 GiB4,653,834,4645.523—Dzluck
Q5_K_S4.33 GiB4,653,834,4645.523—Dzluck
Q5_K_S4.33 GiB4,653,835,2005.523—second-state
Q5_04.33 GiB4,653,835,2005.523—second-state
Q5_K_M4.46 GiB4,785,299,6805.679—Dzluck
Q5_K_M4.46 GiB4,785,299,6805.679—TheBloke
Q5_K_M4.46 GiB4,785,300,4165.679—second-state
Q6_K5.15 GiB5,531,476,1926.565—Dzluck
Q6_K5.15 GiB5,531,476,1926.565—TheBloke
Q6_K5.15 GiB5,531,476,9286.565—second-state
Q8_06.67 GiB7,163,879,6488.502—TheBloke
Q8_06.67 GiB7,163,879,6488.502—Dzluck
Q8_06.67 GiB7,163,880,3848.502—second-state

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0962.00 GiB2.00 GiB—32 / 0 / 0
8,1924.00 GiB4.00 GiB—32 / 0 / 0
16,3848.00 GiB8.00 GiB—32 / 0 / 0
32,76816.00 GiB16.00 GiB—32 / 0 / 0
65,53632.00 GiB32.00 GiB—32 / 0 / 0
131,07264.00 GiB64.00 GiB—32 / 0 / 0

Compare with

same modality, comparable size
vs gemma-4-E4B-itvs Qwen3-4Bvs Qwen3-8Bvs HyperCLOVAX-SEED-Text-Instruct-1.5Bvs Llama-3.2-1B-Instructvs llama-3-youko-8bAll alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 3.53 GiB. The real file is 3.80 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
32
Attention heads
32
KV heads
32
Head dim
128
Hidden size
4096
Vocab
32,256
Sliding window
none
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does deepseek-coder-6.7b-instruct need?
Q4_K_M is exactly 4,083,015,904 bytes (3.80 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is deepseek-coder-6.7b-instruct's KV cache?
16.00 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of deepseek-coder-6.7b-instruct should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.