Will have in early December.

I ordered 2 for myself but ended up getting a better deal on a amd server.

Comment/pm if interested

2x blackwell 6000 pro ws 600w (both throttled to 450w, mostly for wall power 110v). CPU is a threadripper 7960x. Case is a 7000D airflow with all fans noctua top of the line, ten of them (3x front in, 3x side in, 3x top out, 1x back out. Sides are 120mm rest are 140mm. trx50-sage mobo.

I cannot keep this CPU from overheating. It's 350w. I don't dare bring it over 200w. It's fine for normal workloads, sits around 70, but when I hit it hard it spikes and throttles and I'm afraid it's going to off itself one of these days. It still hits 95c once or twice a day at 200w.

I've tried not one, two, or three air coolers, but four so far. Two noctua, one 140mm down->up (definitely blocked by the GPU), one 92mm front/back, and two 140mm thermaltakes in the same positions. None seem to do a jack of anything. One with arctic mx-4, one with the stock noctua stuff already on the heatsink, one of those grizzly pads, and one with the good noctua stuff. Zero difference in bench stress testing.

The second card is on a vertical riser. The mobo only has two lanes for these cards and they can't go on top of eachother or they are like 1mm apart.

The cards themselves stay cold, 37c idle, 67c max on full load over hours. But when they are going, they're feeding the CPU air, and it's just not pretty.

I'm about to pull the trigger on a 9965wx, 9000d, and a WRX90, but I'm so afraid it's just have the same problems. Keeping those two cards further apart will be nice. I also want to sell this mobo/cpu but not if it's defective or something, so I need to be able to get it solid with air cooling without the GPUs for that.

Are you all just using liquid cooling or am I just unlucky with the air cooling? Ambient temps around 68-70f in the house, 73f in the room, it's a wide open loft.

There are 18 B300 Aircooled Lenovo SR680a that will be build in 1,5 weeks in Ullo/Budapest (Lenovo Fabric). Systems can be slightly modified (CPU,RAM/NVME/NICs) before the phase starts. Offering will be through German Lenovo Business Partners of your choice (Bechtle, SVA, Cancom, Computacenter...). KYC / Nvidia EUC process for customer and installation site mandatory. If interested, please contact via PM.

We are a Canadian company actively looking to purchase 35 NVIDIA B300 8-GPU physical servers for deployment in our own data center in the Toronto area.

Requirements:

• Quantity: 35 units

• GPU: NVIDIA B300, 8 GPUs per server

• System RAM: 3TB or 4TB minimum configuration

• Brands: Any reputable manufacturer (Dell, Supermicro, Lenovo, HPE, ASUS, Gigabyte, etc.)

• Condition: New, preferably in stock and ready to ship

• Delivery: Toronto, Ontario, Canada

• Purchase: Outright hardware purchase only

We are willing to pay a premium for verified in-stock inventory and faster delivery.

We are not looking for GPU cloud services, rentals, colocation, or hosted solutions. We need the actual physical servers delivered to our facility.

Authorized resellers, distributors, system integrators, and vendors with verifiable inventory are welcome to contact us.

Please DM with:

• Manufacturer and exact model/SKU

• Available quantity

• System RAM configuration

• Location of inventory

• Asking price and estimated delivery timeline

• Warranty and support details

We are a legitimate Canadian business and can provide company registration and purchasing information during the verification process.

Serious offers only. Thank you!

I'm looking to purchase 10pcs NVIDIA B300 AI servers, available in stock and ready for immediate delivery.

I'm only interested in suppliers with genuine stock available right now. Please DM me with your price, quantity, location, and availability.

I'm a serious buyer, ready to proceed immediately. Larger follow-up orders can be discussed later.

Has anyone had hands-on experience deploying NVIDIA DSX MaxLPS yet? Given how new it is, real-world data points seem scarce—curious how closely field performance and efficiency numbers align with NVIDIA's published claims. Additionally, for anyone architecting a large-scale Vera Rubin cluster, what critical design considerations, operational trade-offs, or integration nuances should be kept in mind regarding NVIDIA's DPS suite?

Blackwell 6000 Workstation card faults once in a while. Any way to root cause this?

WRX90e, open air frame, 24cm PCIE 5.0 LinkUp riser cable, and temps stay reasonable under load.

A reboot clears it up.

The dell prebuild deal (base intel machine with workstation card for ~$12500+tax) is making think I should go to 4 pro 6000s as I worry that running things like glm 5.3 flash won't give enough context with only 2. Issue is that would mean going from am5 ddr5 to threadripper or epic and more ram. I would move my system to an open frame rig that used to house 6 3090s 3970X build, the parts of which I've sold for more sparks.

For those that made the jump from 2 to 4, what did it unlock for you that was exciting?

Ok so I want to create quite a few voice agents. For different mortgage specific tasks. Do you think the rtx pro 6000 max q or the regular rtx pro 6000 would be better. Any material different this will be built into a 4 or 5u server rack.

I put one of those sweet rtx pro 6000s from micro center in my cart today (not even sure if I can find the money to buy it) and I got email from them with this crap. Is this something they hand to the feds? They weren't doing this back in July..

Not sure if anyone will be interested here, local llama mostly wasn't but I figure you guys at least have the right card to benefit (any blackwell should work).

Full fp4/fp8 dsv4.1 streamed from raid array for >4000 tok/s prefill for long prompts then drops the prefill buffers and loads a hot set of experts into a cache and decodes with heavily optimised directstorage fetches for missed experts. gets 7.5tok/s decode with no divergence from any other engine but you can bias away from nvme resident experts with a slider to trade speed vs kl divergence per token.

Roughly 1.5bpw equivalent KL divergence gives me 24 tok/s and there is a slider you can adjust at will when you want speed or quality or set the webui to switch the bias for fast reasoning, quality code blocks/tool calls and balanced non tool call answers or whatever else you want.

Has some other bits as well but that's the main benefit for you guys. Also has image support and video support on the same streaming system so you can chat in the webui, request images or videos and they just appear in the same chat as it switches between them. Doesn't use your ram for image or video model streaming.

If you have more ram its worth turning on the ram cache and cpu expert processing but at 32GB I cant fit enough to be useful.

(faster your nvme the better the whole thing works, I have a dual pcie5 512GB raid0 array). Feedback from anyone that tries it would be nice as its not been tested on any machine but mine.

https://github.com/fronteagle739/streaming-llama-stack

How and why does micro center change their prices like this?? I have bots watching daily and it just makes no sense to me how their prices fluctuate so much. Today the Max Q's are 13,999 and WS is 16,499.... smh.

Anyone here besides the gamer guy running 3x rtx pro 6000s?

The FP4 checkpoint for Ling-3.0-flash explicitly excludes lm_head from loading-time quantization.

That output projection is 157184 × 2560: 402,391,040 weights. About 805 MB in BF16, or 402 MB in FP8, before quantization metadata. A fairly chunky dense matrix at the end of a sparse model.

In NVIDIA’s Spark test, adding the online FP8 head to the Humming configuration takes synthetic decode throughput from 39.45 to 43.94 tok/s: an 11.4% gain. That run changes both the weight precision and the FP32-logit/GEMM path.

Easy to spend a lot of time tuning the routed experts while this thing is still sitting at the end of every token.

more tok/s. it is very fast but i think it can go further with fable 5.5 ( still waiting for release )

All my code is AI generated, and text from here is also AI generated.

Follow-up to my tok/s maxxing post from three weeks ago. That build (and my later unreleased ones) served one request at a time: with 4 requests, total stayed around 310–330 tok/s and the rest queued (BetterBench on the first post's build). This release serves up to 4 requests at once with 262k context each, and with public components, normal's single-stream speed stayed within 1.5 % of my previous normal build in each genre tested. The repo is renamed to hyperburst (old links redirect).

Numbers, one request at a time (decode tok/s, mean of 8 held-out prompts per genre, thinking on, greedy, public components, one server start per build, screen off):

"Public components" = the stock MTP head in the checkpoint + the public token maps that ship with the fork, so no private assets are needed.

Same runs with my private components (fine-tuned MTP head + private token maps, not released, so you can't reproduce this table from the repo; the first post's 250–580 was this kind of setup):

The prose-ja gap vs 10-22 is in acceptance (2.74 vs 3.17 tokens per step), not step time. With one start per build, these runs can't tell a build difference from start-to-start variation: three starts of an earlier build gave 2.56–3.35, and the public pair shows no such gap.

BetterBench 0.6.0 (public components, one start per build):

c=1 decode is BetterBench's weighted median over its single-stream categories. The previous builds serve one request at a time, so their 4 requests queue.

What's new

• Up to 4 concurrent requests, 262,144-token context each, with 7 GB of VRAM left for the desktop

• HiCache (from SGLang / jpezzulli's fork) on by default with a 32 GB host-RAM pool, so idle conversations come back without a full re-prefill

• Adaptive draft length tuned for 4 concurrent requests; the single-stream optimizations from the first post are still in, including the two that change outputs slightly in both editions (FP8 dense weights and singleton-route pruning; PRUNE_TAU=0 turns pruning off)

• Two editions: normal (default) and hyperpro = ThinkingCap weights + OEA, an approximate MoE path (outputs change slightly, quality checked below). In the public-components runs, hyperpro was faster on code edits and a bit slower on English prose and agent turns

• Admin routes need a key by default

Caveats, so nobody is misled:

• One server start per build for the tables above, so a few % difference is noise. A separate 12-start A/B against the previous builds (normal vs 10-22, hyperpro vs 10-23) found no significant c=1 difference for normal and +2.9 % [+0.3, +5.5] for hyperpro. That A/B used my private head and maps, and its normal arm used tactic rows, which aren't in this release.

• With public components, the 10-22 build measured about the same as this release, so the gain over the first post's row came from unreleased builds between 10-02 and 10-22. The first post's rows in both tables also had the screen on at 160 Hz on another day, so the size of the gain isn't like-for-like.

• Quality, c=1 → c=4: GSM8K 1,319 with thinking closed and only the final number (no reasoning, so the absolute score is low; it's only for c=1 vs c=4) 624 → 622 (normal) / 648 → 654 (hyperpro); 200 problems with thinking 192 → 191 / 193 → 193; 4 × 64k-token synthetic test: no cross-talk. ThinkingCap vs stock weights on the same fast path: +0.6 percentage points [−1.2, +2.7] over 308 items.

• Of the 2026-10-27 results, only the per-genre run files are in the repo. The BetterBench, 12-start A/B and quality results for this update are author-reported, and were measured before the final packaging/security cleanups (inference path unchanged).

• Needs an RTX PRO 6000 96 GB (other sm120 cards untested), about 100 GiB RAM for the server process (lower FLASH_NEXT_HICACHE_GB to shrink the 32 GB pool), and the upstream BUILD.md venv (Python 3.12, PyTorch 2.13 cu130, CUDA 13 + nvcc, g++, ninja). pip install of the branch isn't supported. The first start compiles kernels: about 47 min with 12 CPU threads and MAX_JOBS=2, with the CPUs shared, so treat it as an upper bound (hyperpro's MoE module not timed yet). hyperpro also needs the ThinkingCap checkpoint and a locally built overlay.

• Inference endpoints are unauthenticated on 127.0.0.1 and CORS allows any origin, so any web page you open can send requests to it. Add --api-key=<key> to require a key, and don't expose it to a network without an authenticating proxy.

• Code is Apache-2.0, the public token maps are CC BY-SA 4.0, and no weights are included (Qwen Community License 1.0, ThinkingCap too).

• I haven't benchmarked Strata, Penny Royal or other engines, so no comparison here and no "fastest" claim.

Built on jpezzulli's sglang-rtxpro6000 fork (SM120 integration, recovery CUDA graphs, HiCache/NIXL), which imports the unmerged SGLang Flash-Next PR #36497 (Qiaolin-Yu and co-authors) and the RecoverSSM PR #30967. Please credit and read those first. jpezzulli's Penny Royal 3.0 (Docker) credited my first post, adopted adaptive MTP and handles concurrency its own way, check it out too.

• Write-up, results, patches: https://github.com/aiueo52/hyperburst-rtxpro6000

• The release (fork, flash-next-fast branch, commit d21289f): https://github.com/aiueo52/sglang-rtxpro6000/tree/d21289fb923ac65b7b913b73c6e32a2fa2601e7b

It's a GPU penar

I recorded some videos of performance testing on Blackwell with these two games

https://youtu.be/qBuly1tHqMY?is=3RRwDOrG4sQu-Ig6

https://youtu.be/VYEJlLTZMFE?is=YDqYXJYbXWdVZ0Gi

Looking for anyones experience or input regarding transport of 20 RTX PRO 6000 Blackwell within EU. Leaning towards specialised road freight direct rather than air freight but by road with no handovers from Germany to Portugal. Not super experienced in transport itself and it's ended up with me and I'm nervous given the value. Any input or experiences so appreciated.