Gpustack is live · free plan; paid plans on request · last verified 23 Oct 2026 · viability monitor
Gpustack
Turn your own GPU fleet into an OpenAI-compatible token factory with self-hosted MaaS and GPUaaS
For organizations sitting on underused GPUs, GPUStack is the most complete self-hosted way we've seen to expose them as OpenAI- or Anthropic-compatible endpoints without per-token bills. Auto engine selection across five backends plus published Performance Lab numbers (+135% on GLM-4.6, -63% TTFT on Qwen3-8B) back it up with evidence. The price you pay is operational: drivers, networking, and upgrades become your team's job.
Verdict generatedVerified 6d ago · liveness 67/100 · cite: rightaichoice.com/tools/gpustack
- AI platform teams building an internal MaaS service on GPUs they already own
- Enterprise IT managing mixed fleets: NVIDIA, AMD, Ascend, T-Head, Hygon, MetaX
- ML engineers needing on-demand GPU instances with SSH access and persistent storage
- Organizations serving models through OpenAI- or Anthropic-compatible APIs without per-token fees
- Teams wanting a fully managed inference service — GPUStack is self-hosted
- Anyone who needs a no-code interface; setup assumes infrastructure and ML familiarity
- Organizations with no GPU hardware — the platform needs physical accelerators
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Skip GPUStack if you want a managed inference endpoint and have no GPU fleet or DevOps capacity to run Docker, Kubernetes, drivers, and upgrades yourself.
Dedicated accelerators are the real bill: GPUStack replaces per-token fees with the capex, power, and rack space of the H200s, MI300X or Ascend cards you run it on.
GPUStack's open-source core is free to self-host on hardware you already own, which puts its economics closest to teams with sunk accelerator cost — a rack of H200s or a Kubernetes cluster with idle A100s — rather than to teams paying per-token to a managed vendor. The Enterprise Edition adds HA, multi-tenancy, SSO and audit controls for organizations running multiple internal tenants. If you have no GPUs, a managed inference vendor will be cheaper than buying accelerators to run this.
In short
Gpustack — Turn your own GPU fleet into an OpenAI-compatible token factory with self-hosted MaaS and GPUaaS. Best for AI platform teams building an internal MaaS service on GPUs they already own, Enterprise IT managing mixed fleets: NVIDIA, AMD, Ascend, T-Head, Hygon, MetaX, ML engineers needing on-demand GPU instances with SSH access and persistent storage. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is Gpustack?
Gpustack — Turn your own GPU fleet into an OpenAI-compatible token factory with self-hosted MaaS and GPUaaS. Best for AI platform teams building an internal MaaS service on GPUs they already own, Enterprise IT managing mixed fleets: NVIDIA, AMD, Ascend, T-Head, Hygon, MetaX, ML engineers needing on-demand GPU instances with SSH access and persistent storage. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
GPUStack is a self-hosted AI infrastructure platform that runs model inference (MaaS) and GPU compute (GPUaaS) off one control plane. AI platform teams, enterprise IT, and ML engineers point it at accelerators they already own — on-prem servers, Kubernetes clusters, or auto-scaled cloud GPU instances — and serve models through OpenAI-compatible and Anthropic-compatible endpoints rather than paying per-token rates. The hardware coverage is broader than most: NVIDIA H200/H100/A100, AMD MI300X/Radeon/Ryzen, Ascend 910B/910C/310P, T-Head PPU, Hygon DCU, MetaX C500/C550/C600, Moore Threads MTT S5000, Cambricon MLU370/MLU590, and Iluvatar, with builds for Ubuntu, RedHat, OpenEuler, and Kylin on x86_64 and ARM64. The workflow takes four steps. Connect Hugging Face, ModelScope, or local model files. GPUStack maps the hardware to the best-matching engine version among vLLM, SGLang, llama.cpp, TensorRT-LLM, and MindIE. Scale large models across nodes with tensor parallel, pipeline parallel, vLLM Ray clusters, or MP distributed mode. Then serve. Recent coverage shows day-0 work continuing: GPUStack doubled GLM-5.3-Flash throughput from 24.3 to 51.3 tok/s across four DGX Spark nodes using vLLM, MTP speculative decoding, and RoCE tuning, and published a DeepSeek-V4-Flash-DSpark day-0 benchmark where speculative decoding roughly doubles throughput. The Performance Lab keeps tuned-versus-baseline numbers public: +135% throughput on GLM-4.6 over 8× H200, -63% TTFT on Qwen3-8B over 8× H100, and +85% on Qwen3-235B-A22B across 16 H100s in pipeline parallel. GPUaaS handles the compute side with the full GPU instance lifecycle, slicing and overcommit, SSH key auto-injection, Jupyter access, and persistent S3/NFS storage with multi-region mount. Enterprise controls cover RBAC, SSO over OIDC/SAML/AD-LDAP, IP allowlisting, scoped API keys, token quotas, and metering on token, request, and GPU time; observability runs through Prometheus and Grafana. Against a vendor-hosted inference
Key features
- Unified MaaS and GPUaaS under one control plane
- Auto-selects inference engine among vLLM, SGLang, llama.cpp, TensorRT-LLM, MindIE
- Serve models via OpenAI-compatible and Anthropic-compatible APIs
- Day-0 deployment for new model releases through customizable backends
- Speculative decoding to raise throughput and cut time to first token
- Distributed inference: tensor parallel, pipeline parallel, vLLM Ray cluster, MP
- GPU partitioning with slicing and overcommit strategies
- GPU instances with SSH key auto-injection and Jupyter access
- Persistent storage via S3 and NFS with multi-region mount
- Virtual model routing for zero-downtime upgrades
- Traffic weight routing with retry and fallback policies
- Multi-cloud GPU auto-scaling on AWS, Azure, GCP, Alibaba Cloud
- Heterogeneous accelerators: NVIDIA, AMD, Ascend, T-Head, Hygon, MetaX, Moore Threads, Cambricon, Iluvatar
- Serving text-to-image and image-editing models via vLLM-Omni
- LLM routing across multiple models with gateway log validation
Behind the verdict
Pick GPUStack when the GPUs already exist and someone on staff can own them. That's the whole decision in one line. An AI platform team with a rack of H100s, or a mixed NVIDIA-plus-Ascend estate, gets a control plane that speaks OpenAI and Anthropic APIs and lets internal groups be metered and quota'd per organization. Where it earns its keep is heterogeneity. Most self-hosted inference stacks quietly assume NVIDIA. GPUStack lists AMD MI300X, Ascend 910B/910C, T-Head PPU, Hygon DCU, MetaX, Moore Threads, Cambricon, and Iluvatar in the same UI, and auto-picks the engine version for what it finds. If you've inherited a mixed fleet, that alone saves weeks. The second reason to pick it: the team ships on day zero. DeepSeek-V4-Flash-DSpark had a day-0 deployment path via configurable backends, and the recent DGX Spark work doubled GLM-5.3-Flash throughput with MTP speculative decoding and RoCE tuning. Those are real published numbers, not slideware. Pass if you have no hardware and no appetite to buy any. GPUStack is self-hosted; it doesn't rent you GPUs from its own pool. Pass too if you need a no-code console — the setup assumes infrastructure and ML familiarity, and it won't hold your hand through driver hell. Small teams without DevOps capacity will feel the upgrades and networking work land squarely on them. The closest alternative is a hosted inference API (Together, Fireworks, or the providers directly). Those remove the ops burden and bill per token. GPUStack inverts that trade: you keep the data and the GPUs, and you own the pager. There's no free lunch in either direction. One caveat from the sources: GPUStack integrates public model providers like OpenAI and Claude, so it can route to hosted models too. That makes it a reasonable front door for a hybrid
Models under the hood
as of 2026-10-23
Limitations
- GPUStack is self-hosted infrastructure: you deploy and operate it yourself across on-premise, cloud, or hybrid environments, owning the drivers, networking, and upgrade path.
- It spans many hardware families (NVIDIA, AMD, Ascend, T-Head, Hygon, MetaX, Moore Threads and more) and multiple inference engines, but engine-to-accelerator support varies by GPU.
- Setup and operation assume comfort with Linux, Docker/Podman, Kubernetes, and distributed inference concepts; the distributed and multi-cloud features add additional orchestration complexity.
as of 2026-10-03
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Is Gpustack still active?
Gpustack is live · free plan; paid plans on request · last verified 23 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Gpustack? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in Gpustack
Checked 8 days agoAcross the latest 5 updates: 4 feature updates and 1 news mention.
GPUStack doubles GLM-5.3-Flash inference on 4 DGX Spark nodes
GPUStack, vLLM, MTP speculative decoding and RoCE tuning raised GLM-5.3-Flash throughput from 24.3 to 51.3 tok/s across four DGX Spark nodes.
Intelligent LLM routing with GPUStack and Jev
Guide to configuring LLM routing across Qwen3 and DeepSeek through GPUStack and Jev, with model selection validated via gateway logs.
Self-hosting Qwen-Image-2.1 with GPUStack and vLLM-Omni
Guide to serving text-to-image, image-editing and transparent-image generation through OpenAI-compatible APIs using vLLM-Omni on GPUStack.
Self-hosting GLM-5.3-Flash on 8× NVIDIA H20 with GPUStack
Deployment walkthrough for GLM-5.3-Flash on eight H20 GPUs with vLLM, enabling multimodal inference and a 512K-context throughput benchmark.
Day 0 benchmark: DeepSeek-V4-Flash-DSpark on GPUStack doubles throughput
DeepSeek-V4-Flash-DSpark adds speculative decoding to DeepSeek-V4-Flash; GPUStack supports Day 0 deployment through customizable backends and versions.
What do users say about Gpustack?
What people actually say about Gpustack — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Gpustack— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 23 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Gpustack 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed who it suits, our verdict, pricing tiers, who should skip it, features, description, summary, pricing page, models, limitations
- — changed features, description, our verdict, who should skip it, pricing tiers, summary
- — changed pricing page, limitations, models, platforms
- — changed SSL certificate
- — changed hidden costs, who should skip it, summary, pricing fit, limitations, pricing tiers, use cases, switching in, description, switching out, setup time, models, features, our verdict, who it suits
- — changed who should skip it, features, integrations, description, pricing tiers, who it suits, summary, our verdict
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Gpustack cost in 2026?
Gpustack: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 23 Oct 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Gpustack's pricing actually pencils out — and where peers do it cheaper.
GPUStack's open-source core is free to self-host on hardware you already own, which puts its economics closest to teams with sunk accelerator cost — a rack of H200s or a Kubernetes cluster with idle A100s — rather than to teams paying per-token to a managed vendor. The Enterprise Edition adds HA, multi-tenancy, SSO and audit controls for organizations running multiple internal tenants. If you have no GPUs, a managed inference vendor will be cheaper than buying accelerators to run this.
Has Gpustack's pricing changed?
No plan price for Gpustack has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Gpustack?
Pick Gpustack if you are AI platform teams building an internal MaaS service on GPUs they already own, Enterprise IT managing mixed fleets: NVIDIA, AMD, Ascend, T-Head, Hygon, MetaX, ML engineers needing on-demand GPU instances with SSH access and persistent storage, Organizations serving models through OpenAI- or Anthropic-compatible APIs without per-token fees.
Look elsewhere if you are Teams wanting a fully managed inference service — GPUStack is self-hosted, Anyone who needs a no-code interface; setup assumes infrastructure and ML familiarity, Organizations with no GPU hardware — the platform needs physical accelerators, Small teams without DevOps capacity to own drivers, networking, and upgrades.
Skip it if Skip GPUStack if you want a managed inference endpoint and have no GPU fleet or DevOps capacity to run Docker, Kubernetes, drivers, and upgrades yourself.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Gpustack actually fits — and what changes day-one when you adopt it.
Point GPUStack at Hugging Face or an internal model directory, let it auto-select vLLM for the H100 nodes, and stand up an OpenAI-compatible endpoint that the internal app team can call without changing their SDK.
Outcome: Internal apps get a drop-in endpoint on hardware already paid for, with Prometheus/Grafana dashboards showing live token rate and queue depth instead of a per-token cloud invoice.
Provision a GPU instance through the GPUaaS side with SSH key auto-injection and a Jupyter Notebook, attach persistent S3 or NFS storage with multi-region mount, and tear the instance down after the run.
Outcome: Data science work happens on scoped, quota-controlled slices of the existing fleet without a separate VM orchestration stack.
Create separate organizations for Finance and R&D, give each its own GPUs, models, API keys and token quotas, wire authentication to corporate LDAP/OIDC/SAML, and enable audit logging with IP allowlisting.
Outcome: Each department self-serves models inside its own default-deny boundary, while IT keeps per-organization quota, rate-limit and chargeback visibility.
Use cases
- Deploy and serve LLMs such as Qwen 3.6, GLM 5.1 and DeepSeek V4 Pro with optimized throughput on mixed GPU hardware.
- Provide on-demand SSH-accessible GPU instances for data science work and fine-tuning.
- Expose OpenAI-compatible API endpoints for internal applications across any of the five supported inference engines.
- Run distributed inference across multi-node clusters of hundreds of GPUs with auto-orchestration.
- Enforce per-organization token quotas and chargeback for department-level GPU allocation.
- Serve multimodal models, embeddings, rerankers, image and speech models under one management layer.
- Auto-scale GPU capacity on AWS, Azure, GCP or Alibaba Cloud when on-premise capacity runs short.
Setup time & first value
How long it actually takes to get something useful out of Gpustack — broken out by persona, not the marketing-page minute.
For a platform engineer on a single Linux box with Docker or Podman, first model served through an OpenAI-compatible endpoint is a same-day task. A Kubernetes cluster with distributed inference and multi-cloud auto-scaling is a multi-day to multi-week project depending on accelerator mix. GPUaaS instance provisioning lands quickly once the control plane is up; enterprise HA, SSO and multi-tenant
Switching to or from Gpustack
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From raw vLLM or SGLang scripts: keep your engine, let GPUStack handle hardware-to-engine mapping, multi-node orchestration and API exposure.
- →From a hosted inference API: redirect app code to GPUStack's OpenAI- or Anthropic-compatible endpoints and serve the same models from your own accelerators.
- →From a single-node llama.cpp setup: add model management, GPU partitioning and persistent storage while keeping the same engine family.
- →From Kubernetes YAML sprawl: move model deployments onto GPUStack's control plane while continuing to run inside your existing cluster.
- →From per-department GPU VMs: replace manual allocation with GPU instances, quotas and chargeback metering.
- ↗To a managed inference vendor: point application code at their OpenAI-compatible endpoint and retire the self-hosted control plane.
- ↗To raw vLLM on Kubernetes: export your serving configuration and run the engine directly if you no longer need multi-tenant governance.
- ↗To a cloud provider's own model service: move inference to AWS, Azure, GCP or Alibaba Cloud managed endpoints and release the accelerators.
- ↗To a commercial MLOps platform: re-register models and endpoints there if you need a code-native, accelerator-agnostic pipeline.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Gpustack”, and we withheld 6: 6 could not be judged, because “Gpustack” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Gpustack.
Official links
What are the best Gpustack alternatives?
The closest alternatives to Gpustack we list are Vllm, Kubeai and Groq. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Vllm | Free | Free | — |
| Kubeai | Free | Free | — |
| Groq | Freemium | Pricing not published | — |
Alternatives to Gpustack
View all
Tools that pair well with Gpustack
Common stack mates teams adopt alongside Gpustack, with the specific reason each pairing earns its keep.
Vllm
vLLM is the open-source LLM inference engine for high-throughput, OpenAI-compatible serving on your own GPUs.
Kubeai
KubeAI is a Kubernetes operator for self-hosted LLM, embedding, reranking, and speech-to-text inference with an OpenAI-compatible API.
Groq
Groq delivers fast LLM inference on its own LPU and LPX silicon through an OpenAI-compatible API.
Gpustack vs its closest rival
Gpustack's closest head-to-head is with Spider Cloud — Choose Spider Cloud if your need is fast, cost-effective web scraping for AI pipelines; its Rust engine and AI extraction make it ideal for structured data at scale. Choose GPUStack if you need to deploy and manage LLM inference on your own GPUs (NVIDIA, AMD, Ascend, etc.) with enterprise governance, accepting a self-hosted setup. They solve non-overlapping needs — data ingestion vs. model serving. Verdict generated
Head-to-head comparisons
Gpustack vs Spider Cloud
Choose Spider Cloud if your need is fast, cost-effective web scraping for AI pipelines; its Rust engine and AI extraction make it ideal for structured data at scale. Choose GPUStack if you need to deploy and manage LLM inference on your own GPUs (NVIDIA, AMD, Ascend, etc.) with enterprise governance, accepting a self-hosted setup. They solve non-overlapping needs — data ingestion vs. model serving.
GeneratedGpustack vs Temporal Ai
Temporal AI is your go-to if you need bulletproof durability, automatic retries, and human-in-the-loop for AI agents or multi-step business processes — think OpenAI-level reliability. GPUStack wins if you're an enterprise team running your own LLM inference on mixed GPU hardware and need unified MaaS/GPUaaS with Day-0 model support. Choose based on your pain point: workflow resilience vs. GPU inference orchestration.
GeneratedGpustack vs Voyage Ai
Voyage AI is the right choice if you need best-in-class domain-specific embedding models for enterprise RAG with low-dimensional vectors and 32K context. GPUStack is ideal if you want to deploy and manage any open-source LLM on your own GPU infrastructure with a unified control plane. They serve different needs: one provides the models, the other provides the infrastructure.
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