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DeepSeek / deepseek_v2

DeepSeek-Coder-V2-Lite-Instruct

15.7 billion parameters, routing 6 of 64 experts per token.

Open in the calculator →

Architecturepublished data
Quick answer
DeepSeek-Coder-V2-Lite-Instruct needs about 10.7 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 18 of 18 common devices, starting with the NVIDIA GeForce RTX 5070 (12 GB). What else fits in 12 GB. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Find the NVIDIA GeForce RTX 5070, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP1631.4 GB22.0 GB – 31.7 GBsize range
Q8_016.7 GB11.7 GB – 17.3 GBsize range
Q6_K12.9 GB9.02 GB – 13.5 GBsize range
Q5_K_M11.2 GB7.56 GB – 13.5 GBsize range
Q5_011.0 GB7.56 GB – 13.5 GBsize range
Q4_K_M9.64 GB6.18 GB – 13.5 GBsize range
Q4_09.14 GB6.18 GB – 13.5 GBsize range
Q3_K_M7.86 GB4.72 GB – 13.5 GBsize range
Q2_K6.22 GB3.61 GB – 13.5 GBsize range

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 18 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507010.7 GB of 12 GBfits~302 tok/sOpen →
AMD Radeon™ RX 9070 XT10.7 GB of 16 GBfits~329 tok/sOpen →
NVIDIA GeForce RTX 408010.7 GB of 16 GBfits~322 tok/sOpen →
NVIDIA GeForce RTX 508010.7 GB of 16 GBfits~431 tok/sOpen →
NVIDIA GeForce RTX 5070 Ti10.7 GB of 16 GBfits~402 tok/sOpen →
NVIDIA GeForce RTX 5060 Ti 16GB10.7 GB of 16 GBfits~201 tok/sOpen →
NVIDIA GeForce RTX 409010.7 GB of 24 GBfits~452 tok/sOpen →
AMD Radeon™ RX 7900 XTX10.7 GB of 24 GBfits~494 tok/sOpen →
NVIDIA GeForce RTX 309010.7 GB of 24 GBfits~420 tok/sOpen →
NVIDIA GeForce RTX 509010.7 GB of 32 GBfits~804 tok/sOpen →
NVIDIA RTX 6000 Ada Generation10.7 GB of 48 GBfits~431 tok/sOpen →
Apple M4 Pro10.7 GB of 64 GBfits~86 tok/sOpen →
Apple M5 Pro10.7 GB of 64 GBfits~91 tok/sOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S10.7 GB of 128 GBfits~132 tok/sOpen →
Apple M3 Max10.7 GB of 128 GBfits~103 tok/sOpen →
Apple M4 Max10.7 GB of 128 GBfits~116 tok/sOpen →
NVIDIA DGX Spark10.7 GB of 128 GBfits~123 tok/sOpen →
Apple M2 Ultra10.7 GB of 192 GBfits~130 tok/sOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs DeepSeek-Coder-V2-Lite-Instruct

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
28 configurations in stock · 5 out of stock
MachineSpeedPer hourPer M tokensWhereRent
1× RTX 306012 GBrecommendedcheapestbest value~161 tok/sFaster than you read$0.036$0.061Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
1× RTX 4060 Ti 16GB16 GB~129 tok/sFaster than you read$0.090$0.19Vast.aiMarketplace · 99.4% reliableRent on Vast.ai
1× RTX 407012 GB~226 tok/sFaster than you read$0.096$0.12Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
1× RTX 309024 GB~420 tok/sFaster than you read$0.12$0.081Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h Rent on Vast.ai
1× RTX 5060 Ti 16GB16 GB~201 tok/sFaster than you read$0.12$0.17Vast.aiMarketplace · 99.1% reliableRent on Vast.ai
1× RTX 3080 Ti12 GB~409 tok/sFaster than you read$0.14$0.092Vast.aiMarketplace · 99.9% reliableRent on Vast.ai
1× RTX 507012 GB~301 tok/sFaster than you read$0.18$0.16Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
1× RTX 3090 Ti24 GB~452 tok/sFaster than you read$0.19$0.11Vast.aiMarketplace · 99.6% reliableRunPod $0.27/h Rent on Vast.ai

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of DeepSeek-Coder-V2-Lite-Instruct is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of DeepSeek-Coder-V2-Lite-Instruct three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-01 at pinned commit e434a23f91ba; parameter count from the safetensors index at the same revision.

Architecture
deepseek_v2
Layers
27
Hidden size
2,048
Attention heads
16
KV heads
16
Feed-forward width
10,944
Vocabulary
102,400
Context ceiling
163,840
RoPE theta
10,000
Experts
64
Experts / token
6
Expert width
1,408
Shared experts
2
Latent KV rank
512

Where the memory goes

tokenembedding× 27 decoder blocksLatent attentioncompressed KV rankfull contextRouted experts6 of 64 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Latent attention projects keys and values into a compressed rank before caching them, which is why its KV cache is a fraction of a comparable dense model.

Open the exact official config.json ↗

DeepSeek-Coder-V2-Lite-Instruct, card by card

One page per device: whether DeepSeek-Coder-V2-Lite-Instruct fits, at which formats, and how fast.

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As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"deepseek-ai-deepseek-coder-v2-lite-instruct","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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<script src="https://llmbottleneck.com/widget.js"
  data-model="deepseek-ai-deepseek-coder-v2-lite-instruct" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “DeepSeek-Coder-V2-Lite-Instruct VRAM and hardware requirements.” Architecture from deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct at revision e434a23f91ba, retrieved 2026-09-01. https://llmbottleneck.com/models/deepseek-ai-deepseek-coder-v2-lite-instruct

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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catalogue 2026-10-03models 327devices 135