DeepSeek’s April 24, 2026 release links official V4 Pro, Flash, Base, and DSpark weights. That makes local inference possible in principle—not automatically simple or consumer-friendly. This guide shows how to verify the exact model, distinguish official files from community conversions, plan memory without inventing a hardware number, and choose a safer hosted path when appropriate.
Important boundary: this website does not offer a DeepSeek V4 download, local installer, managed cluster, GGUF conversion, or Ollama model. External files remain the responsibility of their publisher and operator.
Official artifacts
“DeepSeek V4” describes a family, not one downloadable file. Base weights target further training or research; instruct weights target chat and agent behavior. Pro and Flash have different total and activated parameter counts. The official model card lists the following combinations.
| Official variant | Total / activated | Published precision | Artifact role |
|---|---|---|---|
| V4 Flash Base | 284B / 13B | FP8 mixed | Official base weights |
| V4 Flash | 284B / 13B | FP4 experts + FP8 mixed | Official instruct weights |
| V4 Pro Base | 1.6T / 49B | FP8 mixed | Official base weights |
| V4 Pro | 1.6T / 49B | FP4 experts + FP8 mixed | Official instruct weights |
“FP4 experts + FP8 mixed” is the model-card description for instruct weights: MoE expert parameters use FP4 while most other parameters use FP8. It is not equivalent to an arbitrary 4-bit community GGUF.
Trust chain
Start from DeepSeek’s dated release notice, follow its official collection link, and record the repository owner, revision, files, precision, license, inference folder, and published checksums. This is the strongest available provenance path.
A converter changes storage and runtime compatibility. Verify the source revision, conversion command, quantization method, tokenizer and chat-template handling, split files, hashes, and test results. A popular upload is not automatically official.
Inspect what the tag actually pulls, which base artifact it references, the template and runtime parameters, and whether the current Ollama engine supports the model architecture. Never substitute a similarly named V3 or R1 artifact.
Hardware requirements
A fixed “X GB RAM” claim is unsafe because DeepSeek V4 hardware requirements change with the exact artifact and serving plan. At minimum, usable memory must cover loaded weights, runtime workspace, MoE routing buffers, framework overhead, and a context-dependent KV cache. Long context and concurrency can dominate after the weights fit.
required memory ≈ loaded weights + runtime overhead + KV cache + safety margin
Use measured artifact size as the starting point, reserve a safety margin, and validate peak—not average—memory under your target context, batch, and concurrency.
FP8, mixed FP4/FP8, or a community quantization changes storage and kernel requirements. File extension alone does not prove support.
KV-cache demand grows with tokens, layers, precision, batch, and runtime implementation. One-million-token support is a model limit, not a free memory setting.
A model this large may require tensor, expert, or pipeline parallelism across accelerators. Interconnect bandwidth and topology affect throughput.
CPU or disk offload can lower VRAM demand but may sharply reduce token speed. Report both time-to-first-token and generation throughput.
Confirm kernels, model config, tokenizer, expert routing, precision, and chat encoding in the exact engine version.
Leave capacity for the operating system, model loading peaks, monitoring, concurrent requests, and failed-worker recovery.
Reproducible workflow
Enter through the official V4 release and collection. Record repository, revision, license, model role, precision, and publisher instructions.
List every weight shard and tokenizer/config file. Confirm free disk for download, unpacking, conversion, and a recoverable duplicate.
Compare published checksums where available. If a converter publishes hashes, verify both the source revision and converted outputs. Stop on unexplained mismatch.
The V4 model card points to an inference directory with weight conversion and interactive demos. Use the revision-matched instructions before adapting another runtime.
Use a short context, one request, and a disposable environment. Confirm load, tokenizer, response parsing, memory peak, and exit behavior.
Increase context and concurrency gradually. Record hardware, drivers, runtime commit, flags, prompt, mode, latency, tokens per second, memory, and errors.
Configuration notes
The official V4 Pro model card points local operators to its revision-matched inference folder rather than giving a universal Ollama or llama.cpp command. It recommends temperature = 1.0 and top_p = 1.0 for local deployment, and at least a 384K context window for Think Max. Those are publisher recommendations for the official release, not proof that every community runtime implements the same mode correctly.
The release does not use a standard Jinja chat template; the model card describes a dedicated encoding folder for message encoding and output parsing. Copying a chat template from another model can silently change behavior, reasoning markers, and tool calls.
No. DeepSeek-V4.io does not mirror weights, publish torrents, distribute GGUF files, or operate a local-deployment product. Follow only the publisher-owned release and model-card links on this page.
The official collection publishes model repositories and inference guidance. Treat a GGUF conversion as a community artifact unless the exact file is linked or endorsed by DeepSeek. Check the converter, source revision, quantization, license, and checksum.
A registry tag or Modelfile may exist, but its name does not prove that it is official, complete, or compatible with the published V4 architecture. Inspect the artifact source and runtime support before pulling it.
There is no responsible single number for every artifact. Memory depends on the exact weight files, precision or quantization, tensor-parallel layout, runtime overhead, context length, batch size, and KV cache. Start from measured file size and runtime documentation.
Flash is smaller than Pro but still has 284B total parameters. A community quantization may reduce weight storage, yet memory, unsupported kernels, context cache, and speed can still make a single consumer GPU impractical. Verify a reproducible report for the exact files and hardware.
The official V4 Pro model card states that its repository and model weights use the MIT License. You must still read the license included with the exact repository and revision you download; do not transfer terms from a similarly named model.
Use a publisher API or another documented hosted provider when you do not need weight custody. You still need to review data handling, pricing, limits, and provider identity, but you avoid operating the full inference stack.
Source snapshot checked July 22, 2026. Repositories, revisions, runtimes, and community artifacts can change.
