u/pmv143

Poor guy dealing with lung cancer and doing his best to stay alert.

Still one of the community favorites on InferX.

Just ran DeepSeek V4 Flash 0731 and hit 421 tokens/sec on this request.

Pretty wild for a model that’s been around for a while.

Just asked it to find a serverless inference provider with sub-second cold starts, even for large models.
This was the answer. 👀

Ran our top model rankings against OpenRouter and Vercel AI Gateway for the last 30 days. Same open-weight models available everywhere, completely different top 10s depending on the platform.

DeepSeek wins across the board — no surprise there. But after that it splits hard. GLM 5.3 Flash is our clear #2, barely shows up on Vercel. Kimi K3 shows up on Vercel’s list, nowhere near ours.

Chart attached. Curious what other platforms/inference providers are seeing — is this workload-driven, community-driven, or just noise at this sample size?

We’re doing a little Weekend Inference.

DeepSeek V4.1 Flash is 90% off on InferX for the weekend.

No big announcement. Just cheaper tokens for anyone who wants to build, benchmark, or burn through some context.

Have fun. 😎

Try here.

Over the same period:

GLM 5.3 Flash
• 111% more requests
• 70% more input token volume
• 94.87% cache hit

DeepSeek V4.1 Flash
• 94.85% cache hit

Users seem to be voting with their workloads.

Yes. We’re making DeepSeek V4.1 Flash free to use on InferX.

V4.1 is a massive model with some impressive capabilities, and we want more people to experience it on their own workloads.

Benchmarks only tell you so much. So we’re opening it up for free and letting you see how it actually performs for you.

Build with it. Code with it. Push it.

Let us know what you think of V4.1 Flash.

Update: we’ve made it free for 48 hours and some tremendous response that our cluster got overwhelmed. It’s now available at 60% off. Cheaper than any provider out there. Try it out.

DeepSeek V4.1 Flash is available now on InferX at 60% off.

Pricing:
Input: $0.12 / 1M tokens
Output: $0.48 / 1M tokens
Cached input: $0.0024 / 1M tokens

Model: deepseek-v4.1-flash

Would love to hear how it performs on your coding and agent workloads.

Try here

We’ve been serving both DeepSeek and GLM-5.3 Flash on InferX, and GLM-5.3 Flash is quickly becoming one of our most interesting workloads.

Over the last 24 hours, we’re seeing roughly a 94% cache hit rate(next closest is 89% on OpenRouter) on GLM-5.3 Flash across real production traffic, not a single synthetic workload.

We’re also currently serving it at 70% off the standard price.

DeepSeek has dominated open-model inference traffic for a while, but based on what we’re seeing, I think GLM-5.3 Flash has a real shot at dethroning it.

GLM-5.3 Flash has officially overtaken DeepSeek as the most-used model on InferX.

Over the last 24 hours:
44,030 requests
2.69B prompt tokens
2.56B cached tokens
95.4% cache hit rate

And we’re currently serving GLM-5.3 Flash at 70% off our listed price.

Pretty interesting to watch the shift happen in real time. DeepSeek had been our top model, but GLM-5.3 Flash usage has now passed it.

The more, the merrier. We’ve added more free models to InferX:

• Gemma 4 31B FP8
• GPT-OSS 20B
• Qwen3.6 35B A3B FP8
• Agents A1
• Devstral 2 123B
• Qwen3 Coder Next FP8

The interesting part isn’t just that they’re free. It’s how we’re serving them.

Instead of dedicating a GPU to every model and keeping it warm 24/7, InferX can keep models off GPU while idle, bring them up with sub-second cold starts when requests arrive, and scale them back to zero when demand disappears.

That lets us run more models on the same GPU infrastructure while keeping utilization high.

Everything is served through the InferX secure container runtime.

This is also the same runtime we can deploy on your GPUs. If you have idle or underutilized GPU capacity and want to try it, DM us.

More free models coming.

deepseek-v4-flash-vision-exp is now available, 40% off.

Matches DeepSeek V4 Flash on text (agents, reasoning, world knowledge), adds vision on top. On multimodal agent benchmarks, it’s a big step up from the base model — close to Opus-4.8 territory.

OpenAI-compatible, drop-in endpoint swap: inferx.net

Ran a 24-hour endurance test on GLM-5.3 Flash on InferX. Sustained numbers from the run:

• Throughput: 27,500+ input tokens/sec
• Requests: 34K+ completed
• Cache hit rate: 95%
• Volume: 2.38 billion input tokens

The cache hit rate is the interesting part — by holding 95%, we're skipping most of the repeated attention work agents generate when they resend context. Same hardware, far less recompute per follow-up request.

For context, per OpenRouter's own provider data, that's the highest cache hit rate of any provider running GLM-5.3 right now — ahead of Z.ai, the model's official host, at 89.0%.

A lot of people are questioning the “local-friendly” claims because RAM prices are insane right now. Fair point.

Here’s the actual reason some of us are still saying it:

The model has a big ~51B n-gram table on top of the main MoE. That table is only sparsely accessed — the model just hashes the recent tokens, pulls a few vectors, and injects them. So a large portion of the total size can realistically be offloaded to system RAM instead of sitting in VRAM.

That changes the hardware picture. Instead of needing pure high-VRAM cards or full datacenter iron, it becomes more practical on:
• 128GB+ unified memory machines
• Multi-GPU setups with a solid amount of system RAM

You’re still looking at serious money, but for a model with this kind of capacity and only ~6B active parameters, the offload potential is what makes the “local-friendly” label make sense relative to other frontier options.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

Qwen3.8-Flash-Next (~125B-A6B + 51B n-gram) memory estimate:

Ideal 4-bit quant ≈ 82 GB
(58 GB main weights + 24 GB n-gram tables)
Real-world quants likely land in the 80–90 GB range.

The big n-gram table is sparsely accessed → excellent candidate for system RAM offload.

This architecture could be surprisingly local-friendly once the weights drop.

Running Qwen 3.8 27B completely free for the next 72 hours — no catch, just want more people to try it out.

Quick note: this is shared infrastructure, so performance may vary depending on load. Zero data retention on all requests.