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    Muse Glimmer 30B

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    Deployed on AWS
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    A self-hosted, production-ready Muse Glimmer 30B model deployed into your AWS environment with a single click. Because everything runs entirely within your private cloud, your data stays secure, isolated, and fully under your control. Best of all, unlimited tokens.

    Overview

    This is a self-hosted deployment of the Muse Glimmer 30B large language model. It runs as a single GPU-powered EC2 instance allowing you to keep your data private and leverage unlimited tokens. Access is over HTTPS on port 443 with a per-instance API key; Ollama itself listens only on localhost. GPU instance types need EC2 quota for "Running On-Demand G and VT instances" (L-DB2E81BA), which new AWS accounts often have set to 0. Once the instance is powered on, the model is ready in about 6 minutes. Highlights of the Muse Glimmer model include:

    • 30-billion-parameter dense causal transformer with a dedicated ~1.8B perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks.

    • Context length of 131,072+ tokens. Input modalities are text and image; output is text.

    • Reliable tool use, multi-step reasoning, and failure recovery for long-horizon agent workflows, including OpenClaw and Hermes Agent scaffolds.

    • Controllable reasoning strength (low / medium / high / xhigh) and multilingual training on data from more than 100 languages.

    • Published high-reasoning scores including 75.5 on MCP Atlas (Public), 51.2 on SWE-Bench Pro, and 76.0 on SWE-Bench Verified.

    • Fully open-source under the Apache 2.0 license.

    Highlights

    • Data security, privacy, and confidentiality
    • Predictable cost
    • Unlimited usage of a dedicated model

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Ubuntu 26.04

    Deployed on AWS
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    Pricing

    Free trial

    Try this product free for 5 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.

    Muse Glimmer 30B

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (6)

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    Dimension
    Cost/hour
    g6.xlarge
    Recommended
    $0.09
    g6e.xlarge
    $0.09
    g6.2xlarge
    $0.09
    g5.xlarge
    $0.09
    g5.2xlarge
    $0.09
    g6e.2xlarge
    $0.09

    AI Insights

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    Dimensions summary

    You pay by the hour for the GPU instance that runs this self-hosted model in your AWS account. The six options are EC2 instance sizes, not feature tiers. They split into three families: g5, g6, and g6e. Within each family, the xlarge size gives you one set of compute resources, and the 2xlarge size gives you more. You pay for the machine you choose, not for tokens or usage volume. Pick the instance that matches your performance needs, and billing accrues for each hour it runs.

    Top-of-mind questions for buyers

    Each label is an EC2 GPU instance type that runs the model in your AWS account. You pay for the running machine, not for tokens or request volume. The g6e, g6, and g5 families use different GPU hardware, and the 2xlarge sizes carry more compute than the xlarge sizes.
    The hourly software fee accrues only while the instance runs. Stopping the instance halts the hourly charge. Note that stopped instances may still incur underlying AWS storage fees for attached volumes, which are separate from the software fee you see here.
    No automatic change happens. You choose and launch the instance size you want. The hourly rate matches whichever instance you run. To move from an xlarge to a 2xlarge, you launch the different instance, and billing then follows that instance's rate.
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    Vendor refund policy

    Refunds may be considered on a per-case basis. Please contact us at support@salientengineering.com  for inquiries.

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    Legal

    Vendor terms and conditions

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    Usage information

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    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Version release notes

    HTTPS on port 443 with a per-instance API key (Bearer). Ollama now listens on 127.0.0.1:11434 only and OLLAMA_ORIGINS is no longer "*" (set SALIENT_ALLOWED_ORIGINS to allow browser apps). The baked model is read off EBS in parallel at boot, so it is ready in about 6 minutes instead of up to 90. The EC2 system log and the SSH login banner show the endpoint, API key, TLS fingerprint and example requests. Added instance types: g6.xlarge, g6.2xlarge, g5.xlarge, g5.2xlarge, g6e.2xlarge.

    Upgrading from an earlier version: clients that called http://:11434 must switch to https:// with the API key, or set SALIENT_EXPOSE_OLLAMA_HTTP=1 in user data to keep the old plain-HTTP listener.

    Additional details

    Usage instructions

    Before you launch: GPU instance types need the EC2 quota "Running On-Demand G and VT instances" (quota code L-DB2E81BA) in your Region to be at least the instance's vCPU count (g6.xlarge = 4 vCPUs). New AWS accounts often have 0, and the launch then fails. Check it or request an increase at https://console.aws.amazon.com/servicequotas/home/services/ec2/quotas/L-DB2E81BA  before you subscribe.

    1. Launch on g6.xlarge or another listed type. In the Security Group, allow inbound TCP 443 from the addresses that will call the API, and TCP 22 only if you need SSH.
    2. Wait about 6 minutes. EC2 console > your instance > Actions > Monitor and troubleshoot > Get system log shows "Salient LLM appliance: READY" with the endpoint, API key and examples. Status at any time: curl -k https://<PUBLIC_IP>/healthz
    3. Call the API with the key from the system log (or /home/ubuntu/salient-api-key over SSH): curl -k https://<PUBLIC_IP>/api/tags -H "Authorization: Bearer <API_KEY>" curl -k https://<PUBLIC_IP>/v1/chat/completions -H "Authorization: Bearer <API_KEY>" -H "Content-Type: application/json" -d '{"model":"muse-glimmer","messages":[{"role":"user","content":"Hello"}]}'
    4. The TLS certificate is self-signed: use -k, or pin the SHA-256 fingerprint printed in the system log. For a trusted certificate, set SALIENT_DOMAIN and SALIENT_LETSENCRYPT_EMAIL in user data (needs a DNS record and TCP 80 open), or run sudo salient-enable-letsencrypt DOMAIN EMAIL. Settings (your own API key, allowed browser origins, context length) go in /etc/salient/salient.conf or EC2 user data, for example SALIENT_ALLOWED_ORIGINS=https://app.example.com .

    Resources

    Support

    Vendor support

    The Salient Engineering support team can be reached at: support@salientengineering.com 

    Our team is happy to assist with deployment and configuration issues.

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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