Llm

Everyone running DeepSeek-V4-Flash on DGX Spark hardware today is doing it with community forks: patched vLLM builds, custom images, source-built kernels. The received wisdom is that the stock NVIDIA NGC container cannot serve this model on GB10 (SM121) because upstream vLLM rejects every sparse-MLA attention backend on that architecture (vllm-project/vllm#45317, still open). We ran the experiment with only nvcr.io/nvidia/vllm:26.06-py3 on a two-node DGX Spark cluster (TP=2 over the 200G QSFP link). Policy constraint: no forks, no nightlies, no patched kernels. Here is what actually happens. ...

We run frontier open-weight models on a desktop-class supercomputer. People assume that means we think local models have caught up to Claude, GPT, and Gemini. They haven’t, and pretending otherwise is the fastest way to make a bad architecture decision. This is how we actually compare models, open and closed, local and cloud, and how that comparison plays out for sensitive domains like federal health. The honest starting point: closed frontier still leads As of June 2026, the most capable general models are closed and cloud-hosted. On the Artificial Analysis Intelligence Index, the leaders are Claude Opus 4.8 (~61), GPT-5.5 (~60), and Gemini 3.1 Pro (~57). No open-weight model you can self-host reaches that tier. ...

gpt-oss-120b, a 117B-parameter / 5.1B-active MXFP4 mixture-of-experts model, runs comfortably on a single NVIDIA DGX Spark (GB10, 128 GB unified memory). At ~63 GB of weights it leaves room for a 131K context window, and its reasoning quality makes it a useful deep-reasoning node alongside a faster mid-size daily driver. The catch is that the SM121 configuration is unforgiving. Several defaults and forum recommendations either silently corrupt output or refuse to start. This is the setup that actually works on the stock NGC container, and the traps that cost us the most time. ...

We moved an LLM backend from Claude Sonnet 4.6 to Opus 4.8. The model is configurable through a single environment variable, so this should have been a one-line change. Instead, every call started returning HTTP 400. The error, once we read the full response body: anthropic.BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': '`temperature` is deprecated for this model.'}} Opus 4.8 no longer accepts the temperature parameter. Send it, even temperature=0.2, and the API rejects the whole request. Our code passed temperature on every call (a habit, for slightly more deterministic synthesis), so the model swap broke everything until we stopped sending it. ...

We expose our internal knowledge base over both REST and MCP using fastapi-mcp, which mounts existing FastAPI routes as MCP tools: no second server, no protocol-translation proxy. Two tools matter here: search_kb (semantic retrieval) and ask_kb (retrieval plus an LLM synthesis call that returns a cited answer). search_kb worked flawlessly everywhere. ask_kb failed, intermittently, and the failure was maddeningly opaque. In the MCP client it surfaced as nothing more than: Command failed with no output No stack trace. No error payload. Just silence. And only sometimes. ...

Qwen3-235B-A22B-GPTQ-Int4 is ~118 GB. A single DGX Spark has 128 GB unified memory, enough in theory, but once CUDA overhead and KV cache are factored in, it’s tight. Running it across two Sparks with TP=2 gives headroom for real workloads. Each DGX Spark is a single logical GPU with no NVSwitch. Tensor parallelism across two units means Ray + NCCL over a direct interconnect. This is what the setup looks like and what will silently fail if not configured correctly. ...

Reference for the NVIDIA DGX Spark GB10 Grace Blackwell Superchip (SM121). The DGX Spark shares the Blackwell name with datacenter hardware but is architecturally distinct. A lot of documentation, forum posts, and vLLM flags written for B100/B200 do not apply here. Some actively break things. SM121 is not datacenter Blackwell Feature DGX Spark (GB10 / SM121) Datacenter Blackwell (B100/B200) TMEM No Yes WGMMA No Yes DSMEM No Yes NVSwitch No Yes CUTLASS FP4 Broken: silent garbage output Supported Memory type Unified LPDDR5X (shared CPU+GPU) HBM3e (GPU-only) Memory per unit 128 GB 192 GB GPUs per unit 1 logical GPU 1 GPU When you see forum recommendations or vLLM flags that say “for Blackwell,” verify they’re for SM121 specifically before using them. ...

The DGX Spark GB10 runs SM121, the Grace Blackwell Superchip. It is not the same silicon as datacenter Blackwell (SM100, H100/H200). SM121 lacks TMEM, WGMMA, DSMEM, and NVSwitch. Several vLLM defaults, forum recommendations, and NVIDIA docs written for datacenter Blackwell do not apply, and some actively break things on SM121. This is a reference for what we learned running vLLM 0.19.0 (NGC container nvcr.io/nvidia/vllm:26.04-py3) on two DGX Sparks: single-node and two-node cluster configurations. ...

We built an internal knowledge base server to give our AI agents access to Conselara’s company data: capabilities, past performance, GSA rates, certifications. The idea was straightforward: expose it as an MCP server so any AI client could query it semantically. It worked in Claude Code. It worked nowhere else. What MCP promises The Model Context Protocol is Anthropic’s open standard for connecting AI models to external tools and data sources. The pitch is compelling: define your server once, and any MCP-compatible client can call it. Claude Code has native MCP support. The ecosystem is growing. ...

Federal health research platforms have a specific challenge with AI: the data is sensitive, the accuracy bar is high, and the compliance requirements are real. You cannot send protected health information to a commercial API, and you cannot publish AI-generated content to a national audience without review. But the volume of work, spanning literature, publications, content, and code, is large enough that ignoring AI entirely leaves real efficiency on the table. ...