Reddit User Asks for Updates on Taalas LLM Accelerator Chips
r/LocalLLaMA top day4 days agoHardwareA Reddit user in r/LocalLLaMA is looking for updates on Taalas chips, referencing earlier claims that the company planned to embed or hardcode a mid-tier LLM into its hardware. The post asks what model might be used, when the chip could arrive, and what pricing might look like. The source itself provides no confirmed answers, specifications, launch date, model name, or pricing information.
Bonsai LM 1-bit and 1.58-bit Benchmarks on Jetson Orin Nano Super
r/LocalLLaMA top day4 days agoBenchmarkA LocalLLaMA post benchmarks five Bonsai LM models, from 1.7B to about 8B parameters, on a $250 Jetson Orin Nano Super 8GB using llama.cpp CUDA. The tests compare 7W, 15W, 25W, and MAXN modes across latency, throughput, energy per token, and thermals. The main takeaway is that 25W is usually the best efficiency/performance point for models up to 4B, while Bonsai-8B may favor 15W for lower power.
MooreThreads Releases MusaCoder-27B Code LLM on Hugging Face
r/LocalLLaMA top day4 days agoReleaseMooreThreads, a Chinese GPU semiconductor company best known for its MUSA compute platform, has released MusaCoder-27B on Hugging Face alongside a technical paper on arXiv. The 27B-parameter model is positioned as a code-generation LLM, extending MooreThreads' ambitions beyond hardware into the AI model layer. Its public availability on Hugging Face signals an open-weights approach, making it accessible to local-inference practitioners and researchers evaluating alternatives to Western-origin coding models.
Qwen3.6-MTP-27B on Tesla V100: llama.cpp Throughput Tuning Question
r/LocalLLaMA top day4 days agoBenchmarkA Reddit user is running Qwen3.6-MTP-27B-MTP in Q4_K_M GGUF format with llama.cpp server on a 32GB Tesla V100. They report one peak of 55 tokens per second, but typical throughput is closer to 44-48 TPS. The post asks whether flags such as parallelism, speculative MTP draft settings, KV cache quantization, flash attention, and a 262K context window are limiting performance without improving output quality.
How Useful Is qwopus Compared With Qwen3.6 27B for Coding?
r/LocalLLaMA top day4 days agoOpinionA Reddit user on r/LocalLLaMA asks for practical comparisons between qwopus and Qwen3.6 27B, specifically for coding work. They note conflicting community opinions, with some users calling qwopus worse and others saying it is much better. In their own simple tests, they did not notice clear differences and want feedback from people using these models for agentic coding.
Charting Local LLM Releases: 2025 Was the Peak, Not 2026
r/LocalLLaMA top day4 days agoCommentaryA r/LocalLLaMA community member shared visualizations tracking the volume of local LLM releases over time. Contrary to the perception that 2026 has been an unusually prolific year, the data indicates the actual release peak occurred in 2025. The poster attributes the misperception to the outsized quality improvements in 2026 making it feel more eventful than it quantitatively was.
Anthropic Is Accused of Nerfing Fable for Other LLM Development
r/LocalLLaMA top day4 days agoCommentaryA r/LocalLLaMA post claims Anthropic may be intentionally limiting Fable when users ask it to help build other LLMs. The source is a short Reddit post with screenshot context, not a formal benchmark or verified disclosure. Discussion centers on trust in hosted closed models, unclear safety boundaries, and why local or open-weight LLMs may be necessary for serious AI development work.
Unsloth releases GGUF version of Cohere North-Mini-Code 1.0 (30B A3B MoE) on Hugging Face
r/LocalLLaMA top day4 days agoReleaseUnsloth uploaded a GGUF version of Cohere's North-Mini-Code 1.0 to Hugging Face, making local inference possible for this 30B A3B MoE coding-focused model. The poster links the release to llama.cpp PR #24260, suggesting new architecture support may be required. No benchmarks or test results have been shared yet; this is an early community resource post.
New to Local LLMs: Overwhelmed by Tool Choices, Model Naming, and Quantization
r/LocalLLaMA top day4 days agoTutorialA first-time local LLM user installed ollama on Windows with gemma4 and qwen3.6, but quickly hit a wall of confusion around GUI tool selection, model size tradeoffs, and cryptic quantization naming like Q4_K_M and IQ4_XS. Despite owning high-end hardware (RTX 5090, 64GB DDR5, 9950X3D), the user lacks the foundational knowledge to make informed choices. The post highlights ongoing onboarding gaps in the local LLM ecosystem, where fragmented tooling and jargon-heavy documentation create steep barriers for newcomers.
Releasing Apodex-1.0 Smol Models (0.8B, 2B, 4B Open-Weights) Optimized for Agentic Verification + AgentHarness Evals
r/LocalLLaMA top day4 days agoReleaseApodex 1.0 launches with open-weight models at 0.8B, 2B, and 4B, trained not for general generation but for specialized sub-agent roles—fact-checking external claims and verifying tool call outputs before passing results to a main controller. The design targets long-horizon agent workflows where routing small tasks to lightweight models avoids wasteful use of 70B+ models at every step. AgentHarness, an open-source evaluation framework for local multi-step agent pipelines, is released alongside the weights.
Furiosa AI inference chip could be a game changer for local LLMs
r/LocalLLaMA top day4 days agoHardwareA r/LocalLLaMA post discusses Furiosa AI’s RNGD inference chip, citing TSMC 5nm, Hynix HBM3, 48GB VRAM, 1.5TB/s bandwidth, and 180W TDP. The author argues it could matter for local LLM users if Furiosa opens its programming interface and works with llama.cpp on a GGML backend. The post later clarifies Furiosa is not selling to consumers; this is a wish and market commentary, not a launch.
RTX PRO 6000 Listed at $13,250 on NVIDIA Official Page
r/LocalLLaMA top day4 days agoHardwareA r/LocalLLaMA post points to NVIDIA Marketplace showing the RTX PRO 6000 Blackwell Workstation Edition priced at $13,250. The post asks when this official-page price appeared, without adding benchmarks or broader pricing evidence. For local LLM users, the figure matters because workstation GPU pricing directly affects the economics of self-hosted inference, experimentation, and small-team AI hardware planning.
OSCAR RotationZoo - Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization
r/LocalLLaMA top day4 days agoPaperOSCAR applies offline-precomputed rotation matrices—derived from spectral covariance analysis—to reshape KV tensor distributions before 2-bit quantization, suppressing outliers and reducing rounding error. The rotation adds negligible inference overhead since it requires no runtime learning. GGUF downloads for Gemma-4-12B-it, Qwen3-32B, and Qwen3-4B-Thinking are available, with llama.cpp and sglang integrations and an arXiv paper.
Releasing Cohere North Mini Code
r/LocalLLaMA top day5 days agoReleaseCohere’s Jay Alammar announced the official release of North Mini Code after early community feedback from r/LocalLLaMA. Weights are available on Hugging Face, including an fp8 version, and the model can be tried for free through OpenCode. For vLLM deployment, Cohere recommends using vLLM main for now and installing cohere_melody for accurate response parsing, while noting community requests for quantization and llama.cpp support.
Cohere North Mini Code 1.0
r/LocalLLaMA top day5 days agoReleaseCohereLabs’ North Mini Code 1.0 appears to have moved from early access to final release, with weights available on Hugging Face. The Reddit post describes it as a 30B A3B coding model. Its Artificial Analysis overall score of 28 trails Qwen 3.6 35B at 43, but its coding index score of 33 is close to Qwen’s 35 and above Gemma 4 26B’s 22.
Unsloth Gemma 4 QAT MTP assistant models now available
r/LocalLLaMA top day5 days agoReleaseA r/LocalLLaMA post notes that Unsloth’s Gemma 4 QAT MTP assistant models are now available in GGUF format. The root directories include q8_0 files named mtp-gemma-4-*.gguf, while MTP folders contain q8_0 and larger quantized variants. The listed releases cover 12B, 26B-A4B, 31B, E2B, E2B mobile, E4B, and E4B mobile it-qat-GGUF repositories.
Single-slot half-height PCIe V100 with NVLink appears in China
r/LocalLLaMA top day5 days agoHardwareA r/LocalLLaMA post says a Bilibili creator has shown a single-slot, half-height PCIe V100 with NVLink on a custom PCB. The card is described as 16 cm long, passively cooled by default, capped at 75W, with another version supporting up to 300W. The 16GB model is expected around or below ¥1500, with a 32GB version reportedly planned, but it is not yet available for purchase.
Rick & Morty
r/LocalLLaMA top day5 days agoCommentaryThis r/LocalLLaMA top-day post is a short image meme titled “Rick & Morty.” The only accompanying text says, “nobody expected HF there,” suggesting surprise at HF appearing in the image’s context. There are no technical claims, model details, releases, or benchmarks, so its value is mainly as a small signal of community culture around Hugging Face / HF and local LLM discussions.
PSA: Throttle GPU Power Limits for Major Energy Savings with Minimal Inference Performance Loss
r/LocalLLaMA top day5 days agoHardwareA Reddit user reminds the local LLM community that throttling GPU power limits offers outsized energy savings with minimal performance cost. On dual Radeon VII cards, cutting power from 250W to 100W per card resulted in less than 10% drop in inference speed. LLM inference is memory-bound rather than compute-bound, making it uniquely tolerant of reduced GPU clock speeds compared to training or rendering tasks.
Rust-native CPU-only LFM2.5-8B-A1B inference library "bebelm" published as cargo crate
r/LocalLLaMA top day5 days agoNew ToolCommunity developer maximecb has published bebelm, a Rust-native, GPU-free inference implementation of Liquid AI's LFM2.5-8B-A1B model, available on crates.io. Decode speed reaches ~37 tokens/s on a Ryzen 7950x with ~7GB memory footprint; prefill is unoptimized and currently similar in speed to decode. The library supports tool-use callbacks, weight sharing across multiple Agent instances with independent KV caches, and Agent cloning to skip repeated prefill on shared prompts.
Jetson Orin NX Build for Hermes Agent + Benchmarking
r/LocalLLaMA top day5 days agoHardwareThe post describes turning an unused Jetson Orin NX into a compact local LLM server for Hermes Agent testing. The goals were low noise, over 10 tok/s generation, 300 tok/s prompt processing, at least 65K context, and a custom case. After testing Gemma 4, Qwen 3.6, and many quant variants, the author reports Gemma 4 26B A4B UD Q2_K_XL reaching 66K context and 10.21 tok/s near 60K context.
TinySearch v0.2.0: Lightweight Open Web-Search Tool for Local LLMs Now Defaults to SearXNG
r/LocalLLaMA top day5 days agoReleaseTinySearch is a lightweight open-source MCP/FastAPI tool that crawls, chunks, and reranks web results into an 8k-token context blob for small local LLMs. Version 0.2.0 replaces DuckDuckGo with SearXNG as the default backend after DDG began rate-limiting and CAPTCHAing automated requests. Users can point it at a self-hosted SearXNG instance; it integrates with Cline, Roo, and OpenCode agent setups.
Anyone seen benchmarks comparing Gemma 4 4-bit QAT vs. 8-bit standard quants?
r/LocalLLaMA top day5 days agoBenchmarkA r/LocalLLaMA user is looking for benchmarks comparing Gemma 4 4-bit QAT models, via Unsloth, against standard 8-bit non-QAT quantized models. They understand QAT is expected to preserve much of the BF16 baseline accuracy, but want hard numbers against traditional 8-bit PTQ. The post highlights scattered feedback but no clear head-to-head evaluation yet.
ggml-webgpu improves prefill speeds for k-quants in llama.cpp PR
r/LocalLLaMA top day5 days agoBenchmarkllama.cpp PR #24225 improves ggml-webgpu matrix multiplication performance for k-quants and refactors matmul paths for Q4/Q5/Q8 and k-quants. In pp512 tests on an M2 Pro, reported speedups range from about 1.33x to 3.78x across Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K. The largest gains appear on Q3_K models, including Qwen and Gemma examples.
Packed twin inference doubles Qwen3.6-27B throughput on one MI50
r/LocalLLaMA top day5 days agoBenchmarkA LocalLLaMA user shared an early packed-twin-inference experiment for local LLM acceleration. The idea resembles speculative decoding, but uses the same quantized model side-by-side instead of a smaller draft model. On a single AMD MI50, the author reports Qwen3.6-27B improving from 19.4 to 38.1 tk/s, with Q8-or-lower quantization as the main target.
JetBrains Mellum 2: a really good and performant model
r/LocalLLaMA top day5 days agoBenchmarkA r/LocalLLaMA user shared informal impressions of JetBrains Mellum 2, focusing on local coding-style tasks and tool calls. On an AMD Radeon RX 7900 XT with llama.cpp Vulkan and 131K context, the model reportedly generated around 111 tokens/s and stayed above 100 tokens/s near full context. The author stresses this is not a scientific benchmark, but a practical workflow-oriented test.
A llama.cpp CLI Command Builder
r/LocalLLaMA top day5 days agoNew ToolA r/LocalLLaMA post introduces a llama.cpp CLI Command Builder with no accounts, email, pop-ups, cookies, or ads. It stores information locally in the browser and includes editable fields for flags and arguments found in the documentation. Users can build CLI or server commands, log run information, and compare which configurations work best for their hardware; only Linux is currently supported.
Pipeline parallelism in llama.cpp may be wasting your VRAM
r/LocalLLaMA top day5 days agoBenchmarkThe author compared three llama.cpp Vulkan builds: default 4 sched copies, 1 sched copy, and no pipeline parallelism. In their Qwen GGUF test, input and output throughput were nearly identical across all configurations. However, the default setting used about 1.5GB more VRAM for compute buffers and reduced usable context from roughly 113K tokens to around 88K, though parallel-request benefits were not tested.
Quick note on recent QAT issues
r/LocalLLaMA top day5 days agoCommentaryThe post argues that recent Google QAT quantization has several implementation problems, including token embeddings being quantized to q6k instead of using a pure mode. It also claims llama-quantize has a hardcoded parameter that mismatches some optimized groups, and that 32-block groups are misaligned. The author recommends Unsloth UD Q4_K_XL as a temporary option and says they are working on a patch.
llama.cpp PR adds MTP support for Gemma-4 E2B and E4B assistants
r/LocalLLaMA top day5 days agoReleaseThe Reddit post links to ggml-org/llama.cpp Pull Request #24282, which adds MTP support for Gemma-4 E2B and E4B assistants. The submitter frames it as useful for tiny Gemma models on phones, low-end machines, Raspberry Pi, or similarly constrained devices. The post does not include benchmarks, merge status, or setup instructions, so it should be treated as a development signal rather than a finished release.