Stuff That Spins
SPIN Processed
Source Reddit r/LocalLLaMA reddit.com Forum
July 5, 2026 community benchmarking community

DeepSeek-V4-Flash in MXFP4 is too slow on CPU

Uses technical specificity (model name, quant format, hardware specs, token/s metric) to imply rigor while omitting essential implementation context: runtime version, compilation flags, kernel optimizations, memory layout, or verification that MXFP4 decoding is active.

View original on reddit.com

Overview

A Reddit user reports unexpectedly low inference speed (3.2 tokens/sec) for DeepSeek-V4-Flash quantized in MXFP4 on CPU-only hardware, contrasting with higher expectations based on GLM-5.2 performance and questioning whether MXFP4 is the bottleneck.

TL;DR

  • User benchmarks DeepSeek-V4-Flash (13B, MXFP4) on legacy Xeon CPU + DDR4, achieving only 3.2 t/s
  • Compares unfavorably to GLM-5.2 (40B, Q4_K_XL) at 1.8 t/s — despite smaller model size and newer quantization
  • Asks whether MXFP4 format is responsible and where to obtain Q4 variants

Key Stats

3.2

tokens/sec

Reported inference speed on E5-2699v4 CPU with DDR4-2133

1.8

tokens/sec

Baseline GLM-5.2 Q4_K_XL speed on same hardware

Questions Answered

What happened?Who is involved?Why does this matter?

Keywords

MXFP4DeepSeek-V4-FlashCPU inferencequantizationtoken throughput

Narrative Frame

performance framing

The Fog

Spin Score

20%

Emphasizes observed slowness and comparative expectation; minimizes uncertainty around whether the reported speed reflects MXFP4’s intrinsic limitations or unreported software/hardware mismatches.

What the story wants you to believe

That the observed slowdown is likely attributable to MXFP4 format limitations rather than configuration, tooling, or runtime issues.

What it makes harder to question

Whether the user’s environment actually supports or correctly executes MXFP4 — shifting focus to the format itself instead of implementation fidelity.

How the spin works

The story redirects attention toward process, intent, scale, mission, or future benefits instead of unresolved concerns. Watch for loaded terms such as miserable performance, disappointing, too slow. The distribution reads as community reporting. A pressure point: llama.cpp or other runtime version used.

Who Benefits If This Frame Spreads

  • u/perelmanych

    Gains visibility, peer validation, and targeted technical assistance

    Framing as a precise benchmark invites expert response and positions the poster as technically competent.

The Frame

Empirical troubleshooting report from an experienced hobbyist deploying frontier models on constrained hardware.

Missing Context

  • llama.cpp or other runtime version used
  • exact quantization toolchain and commit hash
  • whether MXFP4 support is enabled/verified in the runtime
  • memory bandwidth measurement methodology

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details primary

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

The post frames a single-user performance issue as evidence against MXFP4’s viability on CPU

  1. Claim

    The maximum I can get is 3.2 t/s of tg

  2. Frame

    Key details stay obscured

    Empirical troubleshooting report from an experienced hobbyist deploying frontier models on constrained hardware.

  3. Beneficiary

    Gains visibility, peer validation, and targeted technical assistance

    u/perelmanych — Gains visibility, peer validation, and targeted technical assistance

  4. Gap

    llama.cpp or other runtime version used

  5. AI Risk

    AI may repeat: “MXFP4 quantization of DeepSeek-V4-Flash runs slowly on CPU hardware”

    MXFP4 quantization of DeepSeek-V4-Flash runs slowly on CPU hardware.

Claim Ledger

01 Primary Technical Claim Present in Source risk:Low

The maximum I can get is 3.2 t/s of tg

evidence: Self-reported token/s metric

"Unfortunately, the maximum I can get is 3.2 t/s of tg, which is very disappointing."

Evidence Gaps

  • Timing logs
  • Runtime version
  • Memory bandwidth benchmark output
  • Verification that MXFP4 decoding path was engaged

Language Heatmap

Loaded terms that carry the frame beyond the facts.

DeepSeek-V4-Flash in MXFP4 is too slow on CPU

miserable performance Loaded framing

Carries emotional weight beyond the underlying fact.

disappointing Loaded framing

Carries emotional weight beyond the underlying fact.

too slow Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 20%
Evidence Strength 25%
Narrative Risk 25%
AI Repetition Risk 25%
Missing Context Risk 90%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Low

Single-user anecdotal benchmark without reproducible setup details, versioning, or instrumentation; no logs, config files, or timing breakdowns provided.

Verification Status

Claim Present in Source

Narrative Risk

Low

No institutional claim, product launch, or policy implication — purely diagnostic community reporting with no reputational stake beyond individual credibility.

AI Repetition Risk

Low

Source Role & Intent

Reddit r/LocalLLaMA · Forum

Intent: Community Reporting Primary: Troubleshooting Independence: High Spin Weight: Low Trust Weight: Medium

Counter-Frames

Brand Frame

Empirical troubleshooting report from an experienced hobbyist deploying frontier models on constrained hardware.

Media / Reader Counter-Frame

May be dismissed as 'anecdotal' or 'configuration error' without deeper investigation into MXFP4 CPU support gaps.

Regulatory Counter-Frame

Not applicable — no regulatory claims or public safety implications.

AI Summary Frame

May conflate 'slow on one CPU' with 'MXFP4 is unsuitable for CPU inference', ignoring architecture-specific optimization paths.

Missing Voices

Runtime maintainers (e.g., llama.cpp contributors)Quantization tool authors (e.g., Bartowski)Hardware acceleration library engineers

Questions Not Answered

  • Is MXFP4 actually implemented correctly in the inference engine used?
  • What memory bandwidth was measured vs. theoretical peak on this platform?
  • Has MXFP4 been validated for CPU kernels in llama.cpp or equivalent runtimes?

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"MXFP4 quantization of DeepSeek-V4-Flash runs slowly on CPU hardware."

Concern: AI may drop the crucial nuance that this is one user’s unverified observation on specific hardware/software stack — presenting it as a general fact about MXFP4.

  1. Published

    Jul 5, 2026

  2. Ingested

    Jul 5, 2026

  3. SpinGraph Created

    Jul 7, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    —

    Awaiting retention signal

Recall Check Log

No checks yet — recall tracking is opt-in per story.

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

node_id=sts_deepseek_v4_flash_in_mxfp4_is_too_slow_on_cpu

Ask AI about this story

Opens with the SpinGraph .md URL and structured context — one click, prompt included.

ChatGPT Claude Perplexity Gemini Grok

Narrative Entities

DeepSeek-V4-Flash open-weight LLMMXFP4 quantization format

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