What Are the Best Ollama Alternatives for Running LLMs Locally in 2026?

What is ds4 (DwarfStar 4)?

ds4 is a narrow C inference engine for running frontier open-weight models locally on high-memory Mac, CUDA and ROCm machines. Its author is Salvatore Sanfilippo, the GitHub user antirez who created Redis; the project was posted to Hacker News on October 2, 2026 and reached 359 points.

Unlike a general GGUF runner, ds4 supports only a validated set of model families: DeepSeek V4 and V4.1 Flash, GLM 5.x, and Qwen3.8 Flash Next, covering text and vision. Per the project's architecture notes, it uses asymmetric 2-bit quantization that compresses the routed experts to IQ2_XXS/Q2_K while keeping shared experts and projections at high precision, so a roughly 284-billion-parameter mixture of experts fits in 96 to 128GB of unified memory.

One engine exposes three interfaces that share the same model state and cache: ./ds4 for an interactive CLI, ./ds4-server for OpenAI and Anthropic compatible APIs, and ./ds4-agent for persistent coding sessions. The repository is github.com/antirez/ds4, MIT licensed, with 23,446 stars as of October 5, 2026.

Ollama alternatives compared

The six options below cover the realistic range in 2026, from a general-purpose laptop runner to engines built for frontier mixture-of-experts models. Every license, price, and hardware figure was checked on the vendor's own page or GitHub repository on October 5, 2026.

The short read: Ollama is still the easiest starting point, ds4 is what you reach for once you own a 64GB-plus machine, and llama.cpp or vLLM matter when you need control or throughput rather than convenience.

Open source versus paid: what each license actually means

Four of the six are genuinely open source. Ollama, llama.cpp, and ds4 ship under the MIT license, while vLLM and Jan use Apache-2.0. LM Studio's desktop application is closed source, so you are trusting a binary even though the models it runs are open.

Ollama and LM Studio now line up almost exactly on price. Ollama's official pricing page lists Free at $0, Pro at $20 per month or $200 per year ($16.67 per month billed annually), and Max at $100 per month. LM Studio lists Free at $0, Bionic+ at $20 per month, and Pro at $100 per month. Both sell cloud inference credits on top.

Ollama prices that usage per million tokens. Its published table lists deepseek-v4.1-flash at $0.30 input, $0.006 cached input, and $1.20 output, with off-peak pricing outside 12:00 to 18:00 UTC on weekdays and all day at weekends.

The hardware catch: why ds4 needs 64GB and up

Running a 7B or 8B model on a laptop is easy. Running the frontier open weights that ds4 targets is not. According to the ds4 hardware guide, a 64GB Apple Silicon Mac runs Qwen3.8 Q2 with 41.73GiB resident and its 95.37GiB BF16 n-gram table on disk. DeepSeek V4 Flash Q2 is the baseline at 96 to 128GB, GLM 5.3 Q2 and Qwen Q4 also fit at 128GB, and V4.1 Q4 or PRO belongs to the 512GB class. Supported platforms are Apple Silicon with Metal, NVIDIA DGX Spark with CUDA, and AMD Strix Halo with ROCm.

The project's benchmarks show what that hardware buys. On an M5 Max with 128GB, ds4 reports 790.2 tokens per second prefill and 39.4 tokens per second generation at 2,048-token context. On an M3 Ultra with 512GB, the same q2 layout reports 84.4 and 36.9. Prefill collapses faster than generation as context grows, which is why ds4 treats the KV cache as durable data on SSD and resumes long prompts by hash instead of re-prefilling.

When Ollama is still the right choice

For most buyers Ollama remains the default, and nothing about ds4 changes that. It runs on ordinary laptops, its library covers Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, and Gemma, and one command starts a model. On a machine with 16GB of RAM, a general runner is the only realistic option.

ds4 earns its place only if you already own a 64GB-plus Apple Silicon machine or a DGX Spark and want DeepSeek V4 or GLM near full capability. It is deliberately not generic: the documentation states that plain GGUF files are not the target, and the project drops models upstream when a better fit appears for the same machine sizes.

How to choose an Ollama alternative

• You have 8 to 16GB of RAM: stay with Ollama, or try Jan for an offline desktop chat app.
• You want a GUI: LM Studio is the polished desktop choice, free to run locally, with $20 and $100 per month cloud tiers if you also want hosted models.
• You have 64GB or more of unified memory: ds4 is the only option here built specifically for DeepSeek V4 and GLM 5.x at 2-bit, and it is MIT licensed and free to self-host.
• You serve many users: vLLM is the throughput engine under Apache-2.0, but it expects datacenter GPUs.
• You want maximum control: llama.cpp, MIT licensed, remains the reference GGUF runtime other tools build on.

At a glance

Ollama alternatives compared (verified 2026-10-05)

FAQ

Is ds4 free to use?

Yes. ds4 is MIT licensed and free to download and self-host, so your only cost is the hardware. Running DeepSeek V4 Flash Q2 needs 96 to 128GB of unified memory, per the project's hardware guide.

Does LM Studio have a free version?

Yes. LM Studio's free tier runs models locally on your machine at $0. Paid cloud plans start at $20 per month for Bionic+ and $100 per month for Pro, both listed on LM Studio's pricing page and checked on October 5, 2026.

What hardware do I need to run DeepSeek V4 locally?

Plan on 96 to 128GB of unified memory for DeepSeek V4 Flash Q2, and a 512GB-class machine for V4.1 Q4 or PRO. A 64GB Apple Silicon Mac is enough for Qwen3.8 Q2, per the ds4 hardware guide.

Related reading

best local LLM tools, LM Studio Bionic, cheapest GPU cloud for LLM inference, best VPS for AI agents

Sources

• ds4 official site
• ds4 architecture notes (quantization and KV cache)
• ds4 hardware guide
• ds4 benchmarks
• ds4 GitHub repository (MIT license)
• Ollama pricing
• LM Studio pricing
• Ollama GitHub repository
• llama.cpp GitHub repository
• vLLM GitHub repository
• Jan GitHub repository
• GPT4All GitHub repository
• Hacker News launch thread