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deepseek-ai
open-infra-index
Systems & Infrastructure2025–2026
deepseek-ai / open-infra-index

open-infra-index

Production-tested AI infrastructure tools for efficient AGI development and community-driven innovation

Systems & InfrastructureAI & Machine Learning
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15Distinct people
22Mentions
0Explicit recommenders
8.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Production-tested AI infrastructure tools for efficient AGI development and community-driven innovation

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month22 dated mentions

Observed mentions: 2025-02-21 04:24:39 UTC to 2026-10-09 17:58:55 UTC.

012342025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsFeb 20252025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 4 mentions; 3 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsDec 20252026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-10-01 00:00:00 UTC to 2026-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsOct 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
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PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC110
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC211
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC431
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC101
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC202
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC202
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC101
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC202
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC404
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC000
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC000
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC101
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC000
Oct 20262026-10-01 00:00:00 UTC2026-11-01 00:00:00 UTC101

What Hacker News says

22 sources
gpugreg·Oct 9, 2026
> Deepseek also came with heavily subsidized plans, at least at first. Do you have a source for that? I got a source straight from the hoses mouth, which claims that DeepSeek-R1 was highly profitable: > If all tokens were billed at DeepSeek-R1’s pricing (*), the total daily revenue would be $562,027, with a cost profit margin of 545%. https://github.com/deepseek-ai/open-infra-index/blob/main/20... With DeepSeek-V4.1-Flash, the memory requirements for the KV cache have been reduced by over 50x and FLOPS by over 5x, so it is even cheaper: https://arxiv.org/pdf/2609.19969
In Why isn't the industry freaking out about DeepSeek 4.1 Flash?
gpugreg·Aug 4, 2026
Oh, I messed up. Half-way through, I thought it would be a good idea to double the numbers so I don't have to deal with half millions, but forgot to also double the 98.5. Unfortunately, I can not edit it anymore. I think the margins of DeepSeek may be a bit better than with this vibe-coded framework here, since they had the liberty of optimizing their models for their own hardware. For DeepSeek V3, they claimed a cost profit margin of 545%: https://github.com/deepseek-ai/open-infra-index/blob/main/20... At the time, open frameworks were not anywhere close to achieving that number. Not sure whether they caught up. The software wizards at DeepSeek are quite skilled.
In DeepSeek V4 Flash on a Single AMD MI300X
gpugreg·May 28, 2026
> There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is hooked. > I think this is often a mental excuse for continuing to avoid engaging with this tech, in the hope that it will all go away. Thanks for that psychological explanation. I was wondering why people were simply ignoring the math that shows that inference at API pricing can be quite profitable, e.g. published here for DeepSeek V3/R1 with 545% profitability: https://github.com/deepseek-ai/open-infra-index/blob/main/20...
In I think Anthropic and OpenAI have found product-market fit
gpugreg·May 23, 2026
Another factor is that DeepSeek is not just doing inference, but also training models, so they can use underutilized compute nodes for training during off-peak hours, as described in their DeepSeek v3 article: https://github.com/deepseek-ai/open-infra-index/blob/main/20... But I agree that the main driver is that they are really good at optimizing. They will have chosen their architecture in such a way that it will be as efficient as possible on their own infrastructure, so they have a massive head start. Inference framework developers still have to catch up.
In DeepSeek makes the V4 Pro price discount permanent
gpugreg·May 11, 2026
Serving a single user is likely not profitable, but total throughput rises a lot when serving many concurrent users, because the same weights can be used to generate tokens for all users at once, which increases efficiency. Also, a lot of money is being made on input tokens and cached tokens, which are much cheaper to compute. DeepSeek published their math for serving the V3/R1 models. They were 535% profitable: https://github.com/deepseek-ai/open-infra-index/blob/main/20...
In Local AI needs to be the norm
gpugreg·May 2, 2026
I believe that DeepSeek-V4-Pro API at promotional pricing (https://api-docs.deepseek.com/quick_start/pricing) could run at almost exactly 200 % profit. If you take DeepSeek's numbers for DeepSeek-V3 (https://github.com/deepseek-ai/open-infra-index/blob/main/20...) and plug in ~3333 tps/GPU for DeepSeek-V4-Pro (https://developer.nvidia.com/blog/build-with-deepseek-v4-usi...) and a price of $7/hr per B300 GPU, the profit comes out as 202%. The rumor is that Anthropic's Opus models have ~100B active parameters, which is twice as much as DeepSeek-V4-Pro, so inference is at least twice as expensive. Since the API pricing is almost 30 times that of DeepSeek, Anthropic's margins are likely very healthy. But they have to be, since Anthropic has to offset the model training costs, while DeepSeek is backed by High-Flyer Quant. DeepSeek might still
Read the rest
be profitable anyway, but without knowing how much they spent on training and wages, we can't really tell.
In DeepSeek V4 – almost on the frontier
simianwords·Feb 25, 2026
>It could be unfeasible to increase prices so much whenever a new model was released. This means you have no idea what I have been saying. A new model is costlier, but they release mini versions of old models that are way cheaper and compete with older models. GPT 5 mini is way cheaper than GPT 4 but around the same performance GPT-5 mini: Input tokens: ~$0.25 per 1 M Cached input: ~$0.025 per 1 M Output tokens: ~$2 per 1 M ----- GPT-4 (legacy flagship): Input roughly $2.00 per 1 M Output roughly $8.00 per 1 M >Chinese companies are very opaque. I don't pretend to have insight into it. False. The models are not opaque, you can literally download it and host it yourself. They have also released papers on how they reduced cost in certain areas. This is literally them documenting the cost-profit ratio theoretical at 500% https://github.com/d
Read the rest
eepseek-ai/open-infra-index/blob/main/20... >The above statistics include all user requests from web, APP, and API. If all tokens were billed at DeepSeek-R1’s pricing (*), the total daily revenue would be $562,027, with a cost profit margin of 545%. Not only that, there are other providers hosting these opensource models, there are so many companies - just go to openrouter.com So this is your skepticism - openai is subsidising their models so much that each year the keep doing it 20x and eventually reached 100x reduction - all the investors are stupid and they still invest in openai despite unprofitability - employees of openai and anthropic who have claimed that the unit costs are not high are also lying - all other providers are in on the lie - the chinese models like Deepseek is also in on the lie by posting research that is not plausible - the fact that you can run models in your laptop today that beat previous years models is also not enough
In Ed Zitron loses his mind annotating an AI doomer macro memo
versteegen·Jan 18, 2026
Paid/API LLM inference is profitable, though. For example, DeepSeek R1 had "a cost profit margin of 545%" [1] (ignoring free users and using a placeholder $2/hour figure H800 GPU, which seems ballpark of real to me due to Chinese electricity subsidies). Dario has said each Anthropic model is profitable over its lifetime. (And looking at ccusage stats and thinking Anthropic is losing thousands per Claude Code user is nonsense, API prices aren't their real costs. That's why opencode gives free access to GLM 4.7 and other models: it was far cheaper than they expected due to the excellent cache hit rates.) If anyone ran out of money they would stop spending on experiments/research and training runs and be profitable... until their models were obsolete. But it's impossible for everyone to go bankrupt. [1] https://github.com/deepseek-ai/open-in
Read the rest
fra-index/blob/main/20...
In OpenAI could reportedly run out of cash by mid-2027
nl·Dec 24, 2025
Lots of sources, and you can do the math yourself. Here's a few good ones: https://github.com/deepseek-ai/open-infra-index/blob/main/20... (suggests Deepseek is making 80% raw margin on inference) https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch... https://martinalderson.com/posts/are-openai-and-anthropic-re... (there's a HN discussion of this where it was pointed out this overestimates the costs) https://www.tensoreconomics.com/p/llm-inference-economics-fr... (long, but the TL;DR is that serving Lllama 3.3 70B costs around $0.28/million tokens input, $0.95 output at high utilization. These are close to what we see in the market: https://artificialanalysis.ai/models/llama-3-3-instruct-70b/... )
In Local AI is driving the biggest change in laptops in decades
logicprog·Nov 26, 2025
Those are effectively made up numbers, since they're given to him by an anonymous source we have no way of corroborating, and we can't even see the documents themselves, and it contradicts not just OpenAI's official numbers, but first principles analyses of what the economics of inference should be[1] and the inference profit reports of other companies, as well as just an analysis of the inference market would suggest[2] [1]: https://martinalderson.com/posts/are-openai-and-anthropic-re..., https://github.com/deepseek-ai/open-infra-index/blob/main/20... [2]: https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
In Ilya Sutskever: We're moving from the age of scaling to the age of research
ozgune·Aug 28, 2025
I agree that you could get to high margins, but I think the modeling holds only if you're an AI lab operating at scale with a setup tuned for your model(s). I think the most open study on this one is from the DeepSeek team: https://github.com/deepseek-ai/open-infra-index/blob/main/20... For others, I think the picture is different. When we ran benchmarks on DeepSeek-R1 on 8x H200 SXM using vLLM, we got up to 12K total tok/s (concurrency 200, input:output ratio of 6:1). If you're spiking up 100-200K tok/s, you need a lot of GPUs for that. Then, the GPUs sit idle most of the time. I'll read the blog post in more detail, but I don't think the following assumptions hold outside of AI labs. * 100% utilization (no spikes, balanced usage between day/night or weekdays) * Input processing is free (~$0.001 per million tokens) * DeepSeek fits into H
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100 cards in a way that network isn't the bottleneck
In Are OpenAI and Anthropic losing money on inference?
jsnell·Aug 28, 2025
I don't believe the asymmetry between prefill and decode is that large. If it were, it would make no sense for most of the providers to have separate pricing for prefill with cache hits vs. without. Given the analysis is based on R1, Deepseek's actual in-production numbers seem highly relevant: https://github.com/deepseek-ai/open-infra-index/blob/main/20... (But yes, they claim 80% margins on the compute in that article.) > When established players emphasize massive costs and technical complexity, it discourages competition and investment in alternatives But it's not the established players emphasizing the costs! They're typically saying that inference is profitable. Instead the false claims about high costs and unprofitability are part of the anti-AI crowd's standard talking points.
In Are OpenAI and Anthropic losing money on inference?
Denzel·Jun 2, 2025
Thanks for sharing! It's worthwhile to note that https://github.com/deepseek-ai/open-infra-index/blob/main/20... shows cost vs. theoretical income. They don't show 80% gross margins and there's probably a reason they don't share their actual gross margin. OpenAI is the easiest counterexample that proves inference is subsidized right now. They've taken $50B in investment; surpassed 400M WAUs (https://www.reuters.com/technology/artificial-intelligence/o...); lost $5B on $4B in revenue for 2024 (https://finance.yahoo.com/news/openai-thinks-revenue-more-tr...); and project they won't be cash-flow positive until 2029. Prices would be significantly higher if OpenAI was priced for unit profitability right now. As for the mega-conglomerates (Google, Meta, Microsoft), GenAI is a loss leader to build platform power. GenAI doesn't need to be unit pr
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ofitable, it just needs to attract and retain people on their platform, ie you need a Google Cloud account to use Gemini API.
In Cloudlflare builds OAuth with Claude and publishes all the prompts

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