Run DeepSeek-V4-Flash on-premises
○ Memory-fit arithmetic — checkable math, not a benchmark
DeepSeek-V4-Flash is a 284-billion-parameter mixture-of-experts model (~13B active per token) built for long context. At any common precision its weights exceed 128 GB of memory, so it is a model for a larger workstation, a GPU server or a private cloud.
Here is the arithmetic. The right machine depends on your context lengths and number of users; we size it during the AI diagnostic.
Memory fit — why 128 GB is not enough
| Parameters | 284B total, ~13B active per token (mixture-of-experts) |
|---|---|
| Full precision (FP16) | ~568 GB |
| 8-bit weights | ~284 GB |
| 4-bit weights | ~142 GB — already more than 128 GB before any context |
| Fits in 128 GB | no |
| Memory needed | more than 128 GB, plus room for the context cache — a larger workstation, a GPU server or a private cloud |
Same rule of thumb as every page: parameters × bytes per parameter (FP16 ≈ 2, 8-bit ≈ 1, 4-bit ≈ 0.5), plus the KV cache for your context window. Even at 4-bit, ~142 GB of weights is more than a 128 GB machine holds — and long-context work adds cache on top. Only ~13B parameters are active per token, so once it fits, it serves efficiently for its size.
What it serves well
- Heavyweight planning and engineering work where a model that fits 128 GB is not enough.
- Long-context work: large document sets, whole codebases, extended agent sessions.
- The 'company brain' behind an agent team, on a GPU server or a private cloud.
LiveBench scores, dated
Score and rank out of 66 models, per category, in the LiveBench release of 2026-06-25 fetched 2026-10-03.
| Category | Score | Rank |
|---|---|---|
| Reasoning | 70.58 | #64 / 66 |
| Coding | 69.23 | #62 / 66 |
| Agentic coding | 37.63 | #65 / 66 |
| Mathematics | 79.65 | #60 / 66 |
| Data analysis | 68.02 | #54 / 66 |
| Language | 70.12 | #63 / 66 |
| Instruction following | 63.14 | #51 / 66 |
| Global (mean of the 7 categories) | 65.48 | #63 / 66 |
Method note: the global score is the mean of the seven category averages, computed by us from LiveBench's published file; it may differ by a few decimals from the site's own display. Equal scores keep LiveBench's file order. Benchmarks measure benchmark tasks — during an AI diagnostic we re-run the comparison on your own workloads.
Honest limits
- This page is memory arithmetic, not a deployment report: the real footprint depends on the quantization you choose and the context window you need — the AI diagnostic scopes yours.
- A model that needs more than 128 GB is a bigger hardware commitment. If a 122B-class model that fits 128 GB covers your workload, we will recommend that instead — cheaper, simpler, same sovereignty.
Frequently asked questions
Can DeepSeek-V4-Flash run in 128 GB of memory?
No. Even at 4-bit its weights are ~142 GB — more than 128 GB before any context cache is counted. It needs a larger workstation, a GPU server or a private cloud.
What hardware does it need?
Memory comfortably above the ~142 GB of 4-bit weights once the context cache is counted: a larger workstation, a multi-GPU server or a private cloud. The exact machine depends on your context lengths and number of users, and we size it during the AI diagnostic.
Do we need this, or is a smaller model enough?
Most workloads are covered by a model that fits 128 GB, like Qwen3.5-122B or Mistral Large at 4-bit. The larger class earns its keep on long-context, high-complexity planning and engineering work. The assessment answers this honestly for your case — including when the answer is '128 GB is enough'.
Find out which model fits your work
An AI diagnostic maps your processes, classes what is sensitive and sizes the model and the hardware against your real tasks.
Sources
- LiveBench — leaderboard, release 2026-06-25 — accessed 2026-10-03